
The global challenge of tuberculosis (TB) recurrence after successful treatment persists despite national TB programs primarily focusing on patient detection and management of treatment. These programs often overlook the need for followup and intervention posttreatment. Although the incidence of TB in China has been steadily declining, significant efforts are still required to achieve the ENDTB targets. This study utilizes a transmission dynamic model to evaluate the impact of various control strategies on accelerating TB elimination in China. Under the current strategies, TB incidence rates in China are projected to decrease to 44.9 per 100,000 by 2030 and to 40.4 per 100,000 by 2035. Introducing postrecovery interventions to prevent recurrence, along with providing TB preventive treatment (TPT) to 30% of individuals with latent TB infection (LTBI) starting in 2025 and increasing LTBI TPT coverage to 90% beginning in 2031, will contribute to achieving the milestones of the End TB Strategy by 2030 and the 2035 target.
TB continues to pose a significant threat to public health worldwide, including in China. The World Health Organization’s (WHO) End TB strategy targets a reduction in global TB incidence by 80% by 2030 and by 90% by 2035 relative to 2015 levels. In 2022, China reported an estimated 748,000 TB cases, ranking third globally with an incidence rate of 55 cases per 100,000 people. From 2000 to 2022, the annual average percentage change (AAPC) in TB incidence in China was approximately 3.1%, surpassing the global rate of 1.2% during the same timeframe (1). However, this progress remains substantially below the WHO’s target AAPC of 10% by 2025, necessary to meet the 2030 milestone of the End TB Strategy. Although most TB patients can be effectively treated with chemical therapy, the recurrence of TB posttreatment presents a significant hurdle. In 2022, there were over 350,000 cases of recurrent TB globally, constituting 5.5% of the 6.43 million newly reported cases. This issue is particularly acute in highburden countries such as the Russian Federation, where the rate reaches 21%. Recurrent TB often involves more complicated pathologies, increasing the risk of clinical complications and drug resistance, thereby complicating treatment and management, and exacerbating transmission risks. This study aims to develop a dynamic model to analyze TB incidence in China from 2005 to 2022 and forecast trends up to 2030 and 2035, considering both existing strategies and hypothetical scenarios with enhanced measures for recurrence control.
Recurrence was defined as the condition in which patients, previously treated and declared cured or having completed treatment for TB, are diagnosed with a subsequent episode of TB, which could either be a true relapse or a new episode caused by reinfection (2). TPT was defined as the administration of treatment to individuals considered at risk for developing TB disease, with the aim of reducing this risk. This is also known as LTBI treatment or preventive therapy. The WHO recommends TPT for specific target populations， including people living with human immunodeficiency virus (HIV), household contacts of bacteriologically confirmed pulmonary TB patients, and others at heightened risk of TB such as individuals initiating antitumor necrosis factor (antiTNF) treatment, those receiving dialysis, preparing for organ or hematological transplants, or having silicosis. Secondary preventive treatment was defined distinctly from TPT for LTBI, aimed to reduce the risk of recurrence following a successfully treated TB episode.
The compartmental model is a classical mathematical framework utilized to model the dynamics of infectious diseases. It categorizes individuals into distinct groups, with each group sharing average characteristics and typically exhibiting uniform interactions. In this study, a SEIR compartmental model was constructed to analyze the transmission dynamics of Mycobacterium tuberculosis (M. tb), incorporating the risk of recurrence based on a literature review (34). The simulations were conducted using the Epimodel package in R software (version 4.0.3; R Core Team, Vienna, Austria), and the model’s structure is depicted in Figure 1.
Figure 1.The flow diagram of TB transmission dynamic model considering recurrence.
Note: N refers to the total population. α is the birth rate; β is the transmission rate of infectious cases; g is the proportion of slow progression; γ is the progressive rate (divided into γ_{1} and γ_{2} for fast and slow progression) from latent infection to infectious; η is the proportion of successful treatment (divided into η_{1} and η_{2} based on treatment history); f is the proportion of treatment failure (divided into f_{1} and f_{2} based on treatment history); m_{0} is the natural mortality rate; cfr is the fatality rate of infectious due to TB disease (divided into cfr_{1} and cfr_{2} based on treatment history); ρ is the recurrence rate from recovered individuals.
Abbreviation: TB=tuberculosis.
The model delineates several states in the progression of TB. Susceptible (S): Individuals who have not been infected with M. tb. Latent Infection (E): Individuals who are infected with M. tb but have not developed active disease are subdivided into fastprogressing (E_{fast}) and slowprogressing (E_{slow}) categories. Previous research indicates that the lifetime risk of progressing to active TB postinfection ranges from 5%–10%, with approximately 50% developing the disease within the first 2–5 years (γ_{1}) and the others during subsequent periods (γ_{2}). Infectious (I): Active TB cases, split into newly diagnosed, untreated patients (I_{n}) and those with previously treated TB (I_{r}). Recovered (R): Individuals who have either been cured or completed their treatment regimen. The model outlines potential outcomes for TB patients: ① cure or completion of treatment (η_{1}, η_{2}), ② treatment failure (f_{1}, f_{2}), and ③ death attributed to TB (m_{1}, m_{2}) or other causes (m_{0}). Additionally, individuals in the recovered category (R) may suffer a recurrence (ρ), transitioning back to being infectious (I_{r}).
The equations of the model are as follows:
$$\begin{aligned} & \mathrm{N(0)=S(0)+E(0)+I(0)+R}(0)\\& \mathrm{m}_{ \mathrm{0}} \mathrm{+cfr+\eta +f=1}\\& \mathrm{f}_{ \mathrm{1}} \mathrm{=1m}_{ \mathrm{0}} \mathrm{cfr}_{ \mathrm{1}} \mathrm{\eta }_{ \mathrm{1}} \\ & \mathrm{f}_{ \mathrm{2}} \mathrm{=1m}_{ \mathrm{0}} \mathrm{cfr}_{ \mathrm{2}} \mathrm{\eta }_{ \mathrm{2}} \\ & \mathrm{\Delta S=\alpha \beta \times (S \times I/N)m}_{ \mathrm{0}} \mathrm{ \times S} \\& \mathrm{\Delta E}_{ \mathrm{fast}} \mathrm{=(1g) \times \beta \times (S \times I/N)\gamma }_{ \mathrm{1}} \mathrm{ \times E}_{ \mathrm{fast}} \mathrm{m}_{ \mathrm{0}} \mathrm{ \times E}_{ \mathrm{fast}} \\& \mathrm{\Delta E}_{ \mathrm{slow}} \mathrm{=g \times \beta \times (S \times I/N)\gamma }_{ \mathrm{2}} \mathrm{ \times E}_{ \mathrm{slow}} \mathrm{m}_{ \mathrm{0}} \mathrm{ \times E}_{ \mathrm{slow}} \\& \mathrm{\Delta I}_{ \mathrm{n}} \mathrm{=\gamma }_{ \mathrm{1}} \mathrm{ \times E}_{ \mathrm{fast}} \mathrm{+\gamma }_{ \mathrm{2}} \mathrm{ \times E}_{ \mathrm{slow}} \mathrm{I}_{ \mathrm{n}} \\& \mathrm{\Delta I}_{ \mathrm{r}} \mathrm{=f}_{ \mathrm{1}} \mathrm{ \times I}_{ \mathrm{n}} \mathrm{+f}_{ \mathrm{2}} \mathrm{ \times I}_{ \mathrm{r}} \mathrm{\eta }_{ \mathrm{2}} \mathrm{ \times I}_{ \mathrm{r}} \mathrm{(m}_{ \mathrm{0}} \mathrm{+cfr}_{ \mathrm{2}} \mathrm{) \times I}_{ \mathrm{r}} \mathrm{+\rho \times R}\\& \mathrm{\Delta R=\eta }_{ \mathrm{1}} \mathrm{ \times I}_{ \mathrm{n}} \mathrm{+\eta }_{ \mathrm{2}} \mathrm{ \times I}_{ \mathrm{r}} \mathrm{\rho \times Rm}_{ \mathrm{0}} \mathrm{ \times R} \end{aligned} $$ The parameters β, γ1, and γ2, along with the initial values for the compartments E_{fast}(0) and E_{slow}(0), were calibrated in the model using the optim() function, utilizing TB incidence data in China from 2013 to 2022. The aim was to minimize the root mean square error (RMSE) between the predicted and observed data. Initial values for I_{new}(0), I_{ret}(0), and R(0) were derived using data from the Chinese national TB surveillance system, underreporting survey, and the WHO country database. Demographic data were sourced from the China Statistical Yearbook 2023, while treatmentrelated parameters were computed from the national TB surveillance system. Natural history parameters were drawn from prior research (Table 1).
Parameter/compartment Definition Estimated value Resource $ \text{α} $ Birth rate Annual birth rates during 2005–2022 (‰) China Statistical Yearbook (5) $ {m}_{0} $ Natural mortality rate Annual natural mortality rates during 2005–2022 (‰) China Statistical Yearbook (5) $ g $ Proportion of individuals with slow progression among latent infections 91% Ragonnet et al. (3) $ {\eta }_{1} $ Successful treatment rate for new patients 94% National TB surveillance system $ {\eta }_{2} $ Successful treatment rate for retreated patients 85% National TB surveillance system $ {cfr}_{1} $ Case fatality rate for new patients 3% Straetemans et al. (6) $ {cfr}_{1} $ Case fatality rate for retreated patients 9% Straetemans et al. (6);
Mathew et al. (7)$ \beta $ Transmission rate 2.35 Model fitting $ {\gamma }_{1} $ Progressive rate for slow progression LTBI 0.038 Model fitting $ {\gamma }_{2} $ Progressive rate for fast progression LTBI 0.00046 Model fitting $ \rho $ Recurrence rate for recovered patients 0.0005 National TB surveillance system $ N\left(0\right) $ General population in 2013 1,367,260,000 China Statistical Yearbook (5) $ S\left(0\right) $ Susceptible patients in 2013 1,104,396,330 N(0)E(0)I(0)R(0) $ E\left(0\right) $ LTBI in 2013 247,200,608 Gao et al. (8) $ {E}_{fast}\left(0\right) $ Fast progression among LTBI in 2013 20,996,170 Model fitting $ {E}_{slow}\left(0\right) $ Slow progression among LTBI in 2013 226,204,438 Model fitting $ {I}_{new}\left(0\right) $ Newly diagnosed TB patients 884,206 Chinese national TB surveillance system/ underreporting survey/ WHO country database $ {I}_{ret}\left(0\right) $ Previously treated TB patients 81,097 Chinese national TB surveillance system/ underreporting survey/ WHO country database $ R\left(0\right) $ Recovered 14,558,870 Chinese national TB surveillance system/ underreporting survey/ WHO country database Abbreviation: WHO=World Health Organization; TB=tuberculosis; LTBI=latent tuberculosis infection. Table 1. Estimated values of parameters and initial compartments.
Six scenarios, including one baseline and five intervention scenarios to be initiated from 2025, were designed to model current and potential future TB incidence in China. Baseline Scenario: reflects current conditions without additional interventions. Scenario 2 (RecurrenceFree Treatment Scenario): projects a 90% decrease in recurrence rate through novel treatment regimens. Scenario 3 (solely PostTreatment Intervention Scenario): incorporates an 85% reduction in recurrence risk, deploying new vaccines and secondary preventative treatments, along with followup for recovered TB patients. Scenario 4 (Scenario 3 + TPT for 30% LTBI): expands on Scenario 3 by including TPT for 30% of the LTBI population starting in 2025 to expedite progress toward the 2030 End TB strategy milestone. The efficacy of TPT in reducing TB risk among the LTBI population is estimated at 90%. Scenario 5 (Control against Scenario 4): focuses on the LTBI intervention alone as a comparison to Scenario 4 with an identical outcome, increasing TPT coverage to 50% of the LTBI population. Scenario 6 (Scenario 4 + Enhanced TPT for 90% of the LTBI Population Beginning in 2031): Extends Scenario 4 by raising the TPT coverage for the LTBI population to 90% starting in 2031, accelerating achievement of the 2035 End TB strategy goals.
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