Abstract
Background
China is ranked third globally in terms of burden and has a moderately high to high prevalence of tuberculosis (TB). This study meticulously investigated the notification rates of TB and assessed the epidemic in China from 2000 to 2021. The aim of the study was to provide robust supporting data that is crucial for enhancing TB prevention and control strategies.
Methods
Extensive data regarding TB notification rates in China between 2000 and 2021 was collected. The joinpoint regression model was subsequently utilized to assess the temporal trends in the notification rates of TB, which were analyzed through the annual percentage change (APC) and the average annual percentage change (AAPC).
Results
During the study period (2000–2021), the standardized notification rates of TB in China ranged from 38.89/100,000 to 101.15/100,000, with a significant annual average decrease of 4.43% (P < 0.05). Before the COVID-19 pandemic, a marked acceleration in this decline was observed from 2006 to 2015, with an APC of 4.62% (P < 0.05). Stratified by age and sex, the age group with the most significant annual decline in overall standardized notification rates of TB among males in China was < 15 years old, followed by 55–64 years old, and the group with the least decrease was 25–44 years old. Similarly, the age group with the most significant annual decline in standardized notification rates of TB among females was < 15 years old.
Conclusions
The epidemic of TB in China exhibited a downward trajectory between 2000 and 2021. However, it is imperative to prioritize the attention given to males and older adults, and to promote specific and effective prevention and control strategies for these populations.
Keywords: Epidemiological, Tuberculosis, Time trends, Joinpoint
Introduction
Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb), which is transmitted through droplet and aerosol transmission [1]. This disease poses a significant threat to global public health due to the high levels of susceptibility among populations worldwide.
Before 2010, there was a significant decrease in the prevalence of TB in China. However, the burden of TB still remained high. Over the years, five national epidemiological sampling surveys on TB were conducted in China, specifically in 1979, 1984–1985, 1990, 2000, and 2010. The estimated prevalence rates of active TB in China during these surveys were 717/100,000, 550/100,000, 523/100,000, 367/100,000, and 459/100,000, respectively [2–6].
In 2022, an estimated 10.6 million people developed TB. The global incidence of TB was estimated to be 10.33 per 100,000 [7]. The epidemic strength of TB varies among different countries. China accounted for 7.1% of the total global incidence, ranking third among the thirty countries with the highest burden of this disease [7]. During the United Nations General Assembly’s High-Level Meeting on Tuberculosis, world leaders endorsed a political declaration that sets forth ambitious new targets for the next five years in order to advance global efforts towards ending the TB epidemic [8].
Therefore, it is crucial to thoroughly understand the epidemic of TB within national borders in order to effectively prevent and control TB in China. After 2010, data on the prevalence of TB was no longer collected due to the discontinuation of the national TB survey. Nevertheless, the notification rate of TB can also serve as an indicator of the trend and changes of TB in China. This study conducted a piecewise trend analysis to examine the variations in TB notifications in mainland China from 2000 to 2021. The data from the Global Burden of Disease (GBD) was utilized to investigate and compare the temporal trends of notification rates, taking into account sex and age, in China during the specified time period. Joinpoint regression analysis was employed to identify the locations of joinpoints and determine the trends of TB. The findings of this study can serve as evidence for developing strategies of prevention and control of TB.
Methods
Data source
In this study, TB encompassed drug-susceptible TB, multidrug-resistant TB without extensive drug resistance, and extensively drug-resistant TB. The data on the number of newly reported TB cases among males and females in different age groups in China from 2000 to 2021, along with the corresponding total population data, were extracted from the Global Burden of Disease (GBD) database (https://vizhub.healthdata.org/gbd-results/). These data were used to calculate the crude notification rates of TB.
Furthermore, we utilized comprehensive demographic data obtained from the Seventh National Population Census as the standard population. This enabled us to calculate standardized notification rates on TB during the relevant years.
Statistical analysis
This study employed Joinpoint 5.0.2 software in conjunction with R 4.2.1 software for statistical analysis. Reduction rates were calculated for both sexes from 2000 to 2021 using the following formula:
 |
C is reduction rate, a is the standardized notification rates of TB in 2000, b is the standardized notification rates of TB in 2021, and the value of i is male or female.
The fundamental concept of the Joinpoint regression model is to establish segmented regression based on the temporal characteristics of disease distribution. The research time is divided into various intervals using multiple joinpoints, and each interval is fitted and optimized to evaluate the specific characteristics of disease changes within the overall time range more comprehensively [9].
The Joinpoint regression model consists of two components: the linear model (y = xb) and the log-linear model (ln y = xb). For this study, the log-linear model was utilized, and the regression equation is presented below:
 |
In this, y is the standardized notification rate of TB, x is the observation year, e is natural logarithm, k is the number of joinpoints, which is set to 0–5 in this study, τk is the unknown joinpoints, β0 is a constant, β1 is the coefficient of regression, and δk is the coefficient of regression of the k segment piecewise function [10].
The data was divided into multiple grids using the grid search method, which is the default modeling method for Joinpoint. The sum of squares errors (SSE) and mean squared errors(MSE) for each possible case were calculated. The grid points with the lowest MSE were selected as joinpoints to fit the regression function [9, 10].
The Weighted BIC test is a model optimization method recommended in the Joinpoint software as part of the data driven BIC methods. While the Data Dependent Selection (DDS) internally uses BIC or BIC3 based on the empirically determined cut-off values for the selection statistics, the weighted BIC combines BIC and BIC3 using a weighted penalty term based on the data characteristic. That is, it assigns a harsher penalty, making the selection rule close to BIC3, when change sizes are relatively large and a less penalty, making the selection rule close to BIC, otherwise [11].
Within this context, the annual percentage change (APC) indicates fluctuations in trends across distinct intervals, while the average annual percentage change (AAPC) represents an aggregate trend over the entire study duration [10]. These were used to ascertain the trends of incidence of TB across different sexes and age groups in China from 2000 to 2021. Positive APC and AAPC values indicate ascending trends, whereas negative values represent descending trends. The absence of a joinpoint location signifies a continuous trend, either ascending or descending. A P value less than 0.05 was considered to indicate statistical significance [10].
Results
Basic characteristics of the tuberculosis epidemic in China from 2000 to 2021
The standardized notification rates stratified by sex
From 2000 to 2021, a total of 19.87 million cases of TB were reported in China. Of these, 12.29 million (61.85%) were male, and 7.58 million (38.15%) were female. The annual crude notification rates ranged between 43.42 per 100,000 and 84.65 per 100,000 during this period. Moreover, the standardized notification rates varied from 38.89 per 100,000 to 101.15 per 100,000 (Table 1).
Table 1.
Notification rates of Tuberculosis among different sexes in China from 2000 to 2021. (line 15, page 6)
| Year | Male | Female | Overall | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Notification numbers of TB cases (10 thousand) |
Crude notification rates of TB cases (per 100,000 population) |
Standardized notification rates of TB cases (per 100,000 population) |
Notification numbers of TB cases (10 thousand) |
Crude notification rates of TB cases (per 100,000 population) |
Standardized notification rates of TB cases (per 100,000 population) |
Notification numbers of TB cases (10 thousand) |
Crude notification rates of TB cases (per 100,000 population) |
Standardized notification rates of TB cases (per 100,000 population) |
|||
| 2000 | 59.09 | 88.15 | 114.25 | 50.99 | 80.92 | 87.43 | 110.08 | 84.65 | 101.15 | ||
| 2001 | 59.90 | 88.88 | 113.89 | 49.87 | 78.56 | 84.73 | 109.77 | 83.87 | 99.65 | ||
| 2002 | 60.86 | 89.87 | 113.90 | 47.83 | 74.82 | 81.05 | 108.69 | 82.56 | 97.86 | ||
| 2003 | 61.85 | 90.94 | 113.99 | 45.45 | 70.65 | 76.98 | 107.30 | 81.08 | 95.92 | ||
| 2004 | 62.82 | 91.98 | 113.89 | 43.30 | 66.92 | 73.11 | 106.12 | 79.79 | 93.98 | ||
| 2005 | 63.54 | 92.68 | 113.33 | 41.87 | 64.37 | 70.08 | 105.41 | 78.90 | 92.21 | ||
| 2006 | 63.68 | 92.54 | 111.37 | 40.73 | 62.31 | 67.07 | 104.41 | 77.81 | 89.74 | ||
| 2007 | 63.04 | 91.25 | 107.88 | 39.20 | 59.66 | 63.31 | 102.24 | 75.85 | 86.12 | ||
| 2008 | 61.88 | 89.24 | 103.60 | 37.43 | 56.71 | 59.28 | 99.31 | 73.37 | 81.96 | ||
| 2009 | 60.58 | 87.04 | 99.29 | 35.67 | 53.78 | 55.45 | 96.25 | 70.81 | 77.89 | ||
| 2010 | 59.45 | 85.14 | 95.71 | 34.14 | 51.24 | 52.32 | 93.59 | 68.59 | 74.52 | ||
| 2011 | 58.27 | 83.17 | 92.29 | 32.67 | 48.82 | 49.51 | 90.94 | 66.39 | 71.40 | ||
| 2012 | 56.59 | 80.47 | 88.27 | 31.01 | 46.12 | 46.55 | 87.60 | 63.68 | 67.90 | ||
| 2013 | 54.68 | 77.43 | 84.13 | 29.35 | 43.43 | 43.68 | 84.03 | 60.81 | 64.38 | ||
| 2014 | 52.91 | 73.08 | 79.84 | 27.89 | 38.90 | 44.42 | 80.80 | 56.07 | 62.54 | ||
| 2015 | 51.50 | 72.34 | 77.21 | 26.83 | 39.29 | 39.34 | 78.33 | 56.16 | 58.72 | ||
| 2016 | 50.34 | 70.33 | 74.54 | 26.14 | 38.05 | 38.01 | 76.48 | 54.52 | 56.70 | ||
| 2017 | 49.47 | 68.76 | 72.33 | 25.64 | 37.09 | 36.91 | 75.11 | 53.24 | 55.04 | ||
| 2018 | 48.71 | 67.42 | 70.24 | 25.05 | 36.06 | 35.64 | 73.76 | 52.04 | 53.35 | ||
| 2019 | 47.64 | 65.73 | 67.76 | 24.25 | 34.77 | 34.09 | 71.89 | 50.55 | 51.32 | ||
| 2020 | 42.04 | 57.88 | 60.18 | 21.57 | 31.11 | 30.67 | 63.61 | 44.81 | 45.77 | ||
| 2021 | 40.64 | 55.82 | 57.46 | 21.13 | 30.42 | 19.43 | 61.77 | 43.42 | 38.89 | ||
| Total | 1229.48 | 79.73 | 92.32 | 758.03 | 51.49 | 54.93 | 1987.51 | 65.94 | 70.25 | ||
From 2000 to 2021, the average crude notification rates of males and females were 79.73 per 100,000 and 51.49 per 100,000, respectively. The standardized notification rates of males and females were 92.32 per 100,000 and 54.93 per 100,000, respectively (Table 1).
The standardized notification Rates Stratified by Age groups
In the period spanning from 2000 to 2021, an analysis of the standardized notification rates of TB in China revealed significant variations across different age groups. The highest notification rate was observed in the population aged ≥ 65 years (160.67 per 100,000), followed by the age groups of 55–64 years (98.01 per 100,000), 15–24 years (67.70 per 100,000), 45–54 years (61.13 per 100,000), 25–34 years (55.86 per 100,000), and 35–44 years (56.13 per 100,000). Notably, the lowest notification rate was recorded in the population aged < 15 years (19.69 per 100,000) (Fig. 1).
Fig. 1.
The standardized notification rates of Tuberculosis among different age groups
Epidemic trends in Tuberculosis in China from 2000 to 2021
Trend of standardized notification rates among different sexes
A notable decline in the overall notification rate of TB in China was observed during the period spanning from 2000 to 2021, with a reduction of 61.55% and an average annual decrease of 4.43% (P < 0.05). This trend illustrates a consistent decline in notification rates. Specifically, there was a yearly decrease of 1.97% from 2000 to 2006 (P < 0.05), followed by an accelerated reduction of 4.62% from 2006 to 2015 (P < 0.05), a subsequent decrease of 3.23% from 2015 to 2019 (P < 0.05), and a considerable decrease of 12.85% from 2019 to 2021 (P < 0.05) (Fig. 2).
Fig. 2.
Joinpoint regression analysis of the standardized notification rates of Tuberculosis in china. (A) Standardized notification rates of TB cases in overall. (B) Standardized notification rates of TB cases in males. (C) Standardized notification rates of TB cases in females. *: P < 0.05
Further disaggregation of these data by sex revealed a differential trend in notification rates. The overall notification rate among males decreased by 49.71%, whereas the rate among females showed a more substantial decline of 77.78%. There were two joinpoints (2006, 2019) in the trend of notification rate in males from 2000 to 2021, yet there was one joinpoint in the trend in females (Fig. 2). That highlighted a congruent downward trajectory in the standardized notification rates for males and females, aligning with the overall trend. Notably, the average annual decrease in the standardized notification rate was more pronounced in females (APC=-6.67%, P<0.05) than in males (APC=-3.29%, P<0.05).
Trends of standardized notification rates among different age groups
From 2000 to 2021, patients aged < 15 years had the most significant average annual reduction in the overall notification rate of TB in China (AAPC=-5.96, P < 0.05). The other age groups were15-24 years (AAPC=-4.36, P < 0.05), 25–34 years (AAPC=-4.36, P < 0.05), 55–64 years (AAPC=-4.17, P < 0.05), ≥ 65 years (AAPC=-3.94 P < 0.05), 45–54 years (AAPC=-3.64, P < 0.05), and 35-44years (AAPC=-3.47, P < 0.05). Analysis of the data stratified by sex revealed that males aged < 15 years exhibited the most rapid average annual decrease in standardized notification rates during this period (AAPC=-5.29, P < 0.05), while the most minor decrease was noted in the 25–44 years age group (AAPC=-2.62, P < 0.05). Similarly, for females, the most rapid decrease was observed in the < 15 years age group (AAPC=-6.53, P < 0.05), with the most minor decrease occurring in the 55–64 years age group (AAPC=-4.65, P < 0.05) (Table 2).
Table 2.
Trends in the standardized notification rates of tuberculosis among different age groups (line 11, Page 9)
| Age group | Males | Females | Overall | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year | APC/% | AAPC/% | Year | APC/% | AAPC/% | Year | APC/% | AAPC/% | |||||
| <15 | 2000–2010 | -4.58*(-5.00, -3.47) | -5.29*(-5.47 -5.11) | 2000–2005 | -9.46*(-12.14, -8.02) | -6.53*(-6.76, -6.26) | 2000–2005 | -7.40*(-9.26, -6.42) | -5.96*(-6.15, -5.76) | ||||
| 2010–2021 | -5.93*(-6.79, -5.57) | 2005–2011 | -3.47*(-4.45, -0.49) | 2005–2011 | -3.91*(-4.64, -1.52) | ||||||||
| 2011–2014 | -11.16*(-12.53, -8.27) | 2011–2014 | -9.25*(-10.37, -7.16) | ||||||||||
| 2014–2021 | -4.93*(-5.67, -3.04) | 2014–2021 | -5.24*(-5.74, -3.97) | ||||||||||
| 15–24 | 2000–2018 | -1.75*(-2.07, -1.39) | -3.61*(-4.00, -3.28) | 2000–2019 | -4.33(-4.98, 6.30) | -5.28*(-6.15, -3.83) | 2000–2018 | -2.95*(-3.32, -2.50) | -4.36*(-4.86, -3.97) | ||||
| 2018–2021 | -14.02*(-17.29, -10.06) | 2019–2021 | -13.81*(-22.25, -4.62) | 2018–2021 | -12.37*(-18.83, -8.17) | ||||||||
| 25–34 | 2000–2009 | 0.55*(0.06, 1.10) | -2.62*(-2.86, -2.44) | 2000–2005 | -10.14*(-13.49, -8.23) | -6.35*(-6.73, -6.03) | 2000–2005 | -4.49*(-7.05, -3.48) | -4.36*(-4.61, -4.15) | ||||
| 2009–2019 | -3.69*(-4.03, -3.25) | 2005–2011 | -3.07(-4.68, 0.93) | 2005–2010 | -1.36(-2.34, 0.72) | ||||||||
| 2019–2021 | -10.96*(-13.31, -7.82) | 2011–2021 | -6.34*(-8.02, -5.67) | 2010–2019 | -4.52*(-4.97, -3.99) | ||||||||
| 2019–2021 | -10.52*(-12.88, -7.55) | ||||||||||||
| 35–44 | 2000–2007 | 0.68*(-0.03, 1.55) | -2.62*(-2.87, -2.41) | 2000–2005 | -3.59*(-4.74, -0.90) | -4.95*(-5.24, -4.67) | 2000–2006 | -1.06(-1.88, 0.14) | -3.47*(-3.68, -3.24) | ||||
| 2007–2019 | -3.59*(-3.84, -3.15) | 2005–2014 | -6.22*(-8.28, -5.70) | 2006–2019 | -3.97*(-4.19, -2.81) | ||||||||
| 2019–2021 | -7.97*(-10.38, -5.01) | 2014–2018 | -2.08(-3.84, 0.17) | 2019–2021 | -7.26*(-9.35, -4.36) | ||||||||
| 2018–2021 | -7.09*(-10.53, -5.26) | ||||||||||||
| 45–54 | 2000–2006 | 0.94*(0.69, 1.24) | -2.97*(-3.07, -2.89) | 2000–2021 | -5.47*(-6.00, -4.94) | -5.47*(-6.00, -4.94) | 2000–2005 | -0.63(-1.76, 1.05) | -3.64*(-3.82, -3.44) | ||||
| 2006–2015 | -4.43*(-4.76, -4.28) | 2005–2021 | -4.57*(-4.80, -4.35) | ||||||||||
| 2015–2019 | -3.26*(-3.82, -2.56) | ||||||||||||
| 2019–2021 | -7.17*(-8.30, -5.74) | ||||||||||||
| 55–64 | 2000–2005 | -0.68*(-1.33, -0.08) | -3.80*(-3.92, -3.69) | 2000–2006 | -1.62(-3.29, 3.60) | -4.65*(-4.99, -4.22) | 2000–2006 | -1.26*(-1.87, -0.48) | -4.17*(-4.34, -3.99) | ||||
| 2005–2013 | -4.70*(-5.29, -4.39) | 2006–2010 | -9.37*(-12.65, -6.49) | 2006–2010 | -7.05*(-8.45, -5.77) | ||||||||
| 2013–2019 | -3.18*(-3.59, -2.29) | 2010–2021 | -4.52*(-5.08, -3.18) | 2010–2019 | -3.73*(-4.02, -3.01) | ||||||||
| 2019–2021 | -9.52*(-10.89, -7.41) | 2019–2021 | -8.80*(-10.56, -6.23) | ||||||||||
| ≥ 65 | 2000–2005 | 0.26(-3.30, 17.24) | -3.20*(-4.54, -1.99) | 2000–2021 | -4.72*(-5.91, -3.51) | -4.72*(-5.91, -3.51) | 2000–2021 | -3.94*(-4.64, -3.23) | -3.94*(-4.64, -3.23) | ||||
| 2005–2021 | -4.26*(-14.32, -3.64) | ||||||||||||
Discussion
Our findings revealed an average annual decline of 4.43% in the overall standardized notification rate of TB from 2000 to 2021 (AAPC=-4.43%, P<0.05), which was higher than the global decline of 2% observed between 2010 and 2020 [7]. Nevertheless, Indonesia, China, and the Philippines accounted for 10%, 7.1%, and 7.0% of global TB cases, respectively. These countries were renowned for high burden of TB [7]. Previous studies showed that the incidence of TB in Indonesia decreased by 0.98% per year from 2000 to 2020, while in the Philippines, it decreased by 1.9% per year from 1990 to 2021, respectively [12, 13]. These rates were found to be lower than those in China. Notably, prior to the COVID-19 pandemic, there was a marked acceleration in the decrease in the notification rate of TB in China from 2006 to 2015. This trend may be attributed to several pivotal developments, such as the establishment of targeted objectives within TB prevention and control initiatives in China [14, 15], the provision of support through international funding projects [16], and the implementation of a comprehensive and integrated approach to tuberculosis control known as “The Trinity” as part of the Twelfth Five-Year Plan [14]. After establishing goals for the prevention and control of TB, China has enhanced collaboration between different departments and increased financial support. The China Ministry of Health-Gates Foundation TB Control Cooperation Project has played a leading role in validating and evaluating the feasibility and effectiveness of five new diagnostic tools for TB, thereby establishing a platform for their verification and assessment. Furthermore, it has established financing and payment mechanisms for managing treatment of patients with multi-drug resistant TB, integrated with medical insurance. The project has also tested the effectiveness and feasibility of new collaborative models for prevention and control between hospitals and the Centers for Disease Control and Prevention (CDC) [16]. The comprehensive framework for TB prevention and control has refined the roles and responsibilities of the CDC, designated TB medical institutions, and primary healthcare facilities, resulting in more standardized and systematic management of TB patients. These measures have collectively contributed to a rapid decrease in the incidence of TB. However, there was a deceleration in the downward trajectory of the notification rate of TB between 2015 and 2019 in China compared to the preceding years. This shift can be attributed to the adoption of molecular biological techniques [17], for example, WHO extended the utilization of GeneXpert in 2014 [18]. Previous study showed that the implementation of Xpert MTB/RIF as the primary diagnostic test for TB in public health facilities resulted in a substantial increase in notification rates for all cases of bacteriologically confirmed TB [19].
From 2000 to 2021, the standardized notification rate for TB among males in China was higher than that among females, and the average annual decrease was lower for males compared to females. Echoing this gender disparity, research conducted in the South African region indicated a persistently higher incidence of TB in males compared to females, with an increasing ratio over time since 2000, which aligned with our findings [20]. The factors contributing to the gender disparity in the incidence of TB are multifaceted [21]. Previous research showed that gender disparity in TB notification was unlikely to result from social factors, as the accessibility of medical care services for Females was not lower than that for males in low- and middle-income countries [22]. It was more probable that variations in gender-related biological factors and health behaviors contributed to differences in the pathogenesis of tuberculosis. These biological factors include sex hormones and genetic factors. Estrogen was proven to stimulate the production of proinflammatory cytokines such as IFN-γ, TNF-α, and IL-12, while inhibiting a potent anti-inflammatory cytokine (IL-10) [21]. Research suggested that female hormones may have a protective effect against the infection and progression of TB [23]. In addition, males are frequently more susceptible to risk factors, notably smoking and alcohol abuse. The World Health Organization (WHO) identified smoking as an independent risk factor for TB, amplifying the risk by more than 2.5 times [24]. Empirical evidence suggested that smoking is more likely to lead to TB in males compared to females [25]. Compared to individuals who do not smoke, it was observed that smokers have a higher concentration of immature inflammatory monocytes in their lungs. This means that smoking resulted in migration of immature inflammatory monocytes from the periphery to the lungs. As a result, these individuals exhibit elevated inflammatory responses upon exposure to Mtb and accelerated intracellular growth of Mtb [26]. Additionally, the risk of active TB is significantly increased by excessive alcohol consumption, which is defined as consuming over 40 g daily and/or having alcohol use disorder [27]. Alcohol abuse has a negative impact on the immune functions of the alveolar macrophage, which is the resident innate immune effector in the lung. Moreover, chronic alcohol intake also increased oxidative stress in the alveolar space, potentially promoting the growth of Mtb [28]. Consequently, males have emerged as a crucial demographic group in TB prevention and control strategies, with interventions targeting the reduction of smoking and alcohol consumption potentially playing a significant role in curbing the spread of the disease.
Our study revealed significantly elevated standardized notification rates of TB among elderly individuals aged 65 years and over in China, coupled with a relatively more minor average annual reduction and were consistent with those of similar studies conducted in the United States [29]. The TB epidemic is more severe in older adults in China, potentially attributable to a larger pool of infected individuals, a diminished immune response, and a higher prevalence of comorbid chronic conditions such as diabetes in this age group [30]. A tuberculin skin test (TST) and an interferon-γ release assay were used to test for latent infection in China [31]. Previous research indicated that rates of positivity for the tuberculin skin test(TST) and the interferon-γ release assay test gradually increased with age [31]. Over 90% of individuals infected with Mycobacterium tuberculosis (Mtb) are in a latent infection state, and they may develop active TB in the future [32]. That means, the prevalence of latent tuberculosis infection was higher in the elderly, which might explain the trend of TB incidence. Besides, BCG vaccination was included in the national immunization program in China since 1978; therefore, the poor coverage of vaccination in individuals born before that time might also explain the limited effectiveness of the BCG vaccine in adults, especially in the elderly [31]. Consequently, special attention should be given to individuals aged 65 years and over when developing TB prevention and control strategies. The importance of regular annual health examinations, including chest X-rays or CT scans, is accentuated for this population segment.
In summarizing the data from 2000 to 2021, it is apparent that the epidemic of TB in China has demonstrated a substantial downward trend. However, there was a less significant decrease in the incidence of TB among middle-aged and elderly males, indicating a need for targeted attention and the implementation of effective prevention and control measures explicitly tailored to this group.
This study had several limitations. The reporting of tuberculosis (TB) in China relied heavily on a passive system, which also informed the data used in the GBD. One major limitation of this approach was the potential omission of TB patients who did not seek hospital treatment, resulting in an underestimation of TB incidence. To end TB epidemic, reliance on passive detection alone was found to be inadequate [33]; it was essential to enhance screening efforts within high-risk populations and strengthen capacity for case finding. Additionally, it should be noted that reporting standards and diagnostic methodologies may have differed across various years and regions, potentially also leading to an underestimation of the actual incidence. With the ongoing advancements in technology, diagnostic techniques for Mtb are continuously improving. WHO recommended more rapid and convenient molecular biological tests in their consolidated guidelines on TB, which may enhance the ability of case finding [34]. These automated tests enhance the convenience and accuracy of TB diagnostics. Notably, during the COVID-19 pandemic, China made significant advancements in its molecular biological capabilities, especially in PCR testing. Therefore, It is crucial to capitalize on this progress and extensively promote molecular biological techniques for the detection of Mtb in the future. Furthermore, affected by the pandemic of COVID-19, there might be a delay in reporting cases diagnosed in 2020 and 2021, resulting in the omission of these cases and an overestimation of the decline. In addition, the advent of the COVID-19 pandemic after 2020 likely impacted the diagnosis, reporting, and management of TB. Thus, these factors could potentially have an impact on the accurate representation of the epidemic’s trend.
Acknowledgements
The authors thank all medical personnel who contributed to the collection and reporting of TB cases in China.
Author contributions
Zhili Li designed the study. Zhili Li and Lijie Zhang extracted the data and constructed the database. Zhili Li analyzed the data and drafted the manuscript. Yuhong Liu made critical revisions to the manuscript. All the authors have read and approved the final manuscript.
Funding
This study was supported by the Beijing Municipal Commission High Level Public Health Talent Construction Project (Discipline Leader 03–11) and the Beijing Hospitals Authority Ascent Plan (DFL20221401).
Data availability
The datasets supporting the conclusions of this article are publicly available from the Global Burden of Disease ( https://vizhub.healthdata.org/gbd-results/).
Declarations
Ethics approval and consent to participate
No Institutional Review Board approval was sought for this current study since only publicly released data were used and privacy information wasn’t involved. All procedures were performed in accordance with relevant guidelines.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets supporting the conclusions of this article are publicly available from the Global Burden of Disease ( https://vizhub.healthdata.org/gbd-results/).