Article Text
PDF
Abstract
Background Mathematical modelling has been used extensively to estimate the potential impact of new tuberculosis vaccines, with the majority of existing models assuming that individuals with Mycobacterium tuberculosis (Mtb) infection remain at lifelong risk of tuberculosis disease. Recent research provides evidence that self-clearance of Mtb infection may be common, which may affect the potential impact of new vaccines that only take in infected or uninfected individuals. We explored how the inclusion of self-clearance in models of tuberculosis affects the estimates of vaccine impact in China and India.
Methods For both countries, we calibrated a tuberculosis model to a scenario without self-clearance and to various scenarios with self-clearance. To account for the current uncertainty in self-clearance properties, we varied the rate of self-clearance, and the level of protection against reinfection in self-cleared individuals. We introduced potential new vaccines in 2025, exploring vaccines that work in uninfected or infected individuals only, or that are effective regardless of infection status, and modelling scenarios with different levels of vaccine efficacy in self-cleared individuals. We then estimated the relative disease incidence reduction in 2050 for each vaccine compared with the no vaccination scenario.
Findings The inclusion of self-clearance increased the estimated relative reductions in incidence in 2050 for vaccines effective only in uninfected individuals, by a maximum of 12% in China and 8% in India. The inclusion of self-clearance increased the estimated impact of vaccines only effective in infected individuals in some scenarios and decreased it in others, by a maximum of 14% in China and 15% in India. As would be expected, the inclusion of self-clearance had minimal impact on estimated reductions in incidence for vaccines that work regardless of infection status.
Interpretations Our work suggests that the neglect of self-clearance in mathematical models of tuberculosis vaccines does not result in substantially biased estimates of tuberculosis vaccine impact. It may, however, mean that we are slightly underestimating the relative advantages of vaccines that work in uninfected individuals only compared with those that work in infected individuals.
- Vaccines
- Tuberculosis
- Mathematical modelling
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information.
This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
Statistics from Altmetric.com
WHAT IS ALREADY KNOWN ON THIS TOPIC
Recent research provides evidence that self-clearance of Mycobacterium tuberculosis infection may be common. No previous modelling work has investigated the potential effects of self-clearance on vaccine impact, or has explored the effects of the large amounts of uncertainty that exists around the properties of self-clearance.
WHAT THIS STUDY ADDS
We explored how the inclusion of self-clearance in models of tuberculosis (TB) affects the estimates of vaccine impact in China and India. Our work suggests that the neglect of self-clearance does not result in substantially biased estimates of TB vaccine impact, however, it may mean that we are slightly underestimating the relative advantages of vaccines that work in uninfected individuals only compared with those that work in infected individuals.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Our findings can be used to justify the use of evidence from TB vaccine models that do not incorporate self-clearance when making TB vaccine estimates for overall populations.
Introduction
Tuberculosis (TB) is one of the leading causes of death from a single infectious agent worldwide, with an estimated 1.6 million deaths in 2021.1 The majority of countries are not on track to meet the WHO End TB Targets for 2035, which include reductions in the absolute number of TB deaths by 95% and the TB incidence rate by 90%, compared with 2015 levels. Modelling studies suggest that current available interventions are insufficient and that the development of new tools is essential to meet the WHO End TB Targets. The only licensed vaccine against TB, BCG, is effective at preventing disseminated disease in infants, but most studies suggest that it offers limited protection against infectious pulmonary TB in adults.2 The development of new TB vaccines, that protect adults from developing infectious disease, is therefore critical for reducing TB incidence and mortality in line with the End TB Targets.3
There are currently several TB vaccine candidates in the clinical development pipeline, with the potential for diverse vaccine characteristics and indications. A 3-year phase 2b trial of the M72/AS01E candidate vaccine demonstrated an efficacy of 49.7% (95% CI 2.1 to 74.2) for preventing disease in adults who were positive by interferon-gamma release assay at baseline.4 A trial of BCG-revaccination achieved an efficacy of 45.4% (6.4%–68.1%) against the secondary endpoint, preventing sustained infection, in a cohort of IGRA negative adolescents in South Africa.5
Mathematical models have been used extensively to estimate the potential impact and cost-effectiveness of TB vaccines with different specifications.6 Models have, for example, provided age indications for TB vaccines, by demonstrating the greater and more rapid impact before 2050 of targeting adolescents and adults instead of infants in low and middle income countries,7 8 and older adults only in settings such as China.9 Models have also assessed whether greater population-level impact would be delivered by vaccines with efficacy in Mycobacterium tuberculosis (Mtb)-infected populations (often referred to as current infection (CI) vaccines) vs uninfected populations (often referred to as no CI (NCI) vaccines),7 8 10–12 and how population-level impact may vary by age and setting.10 Model results such as these are being used to guide the development and implementation planning of new vaccines, informing, for example, the design of future clinical trials.7 8 13
Most existing models of TB assume that individuals with Mtb infection remain at lifelong risk of progression to TB disease. However, recent research provides evidence that self-clearance of Mtb infection can occur: individuals previously infected with Mtb clearing their infections and ceasing to be at risk of progressing to TB disease (in the absence of reinfection).14 The lack of a test that differentiates between viable and non-viable Mtb infections means that they cannot be directly observed. Evidence for self-clearance is therefore indirect, with the strongest evidence coming from individuals who at one point were immunoreactive (an indication of past Mtb infection), but of whom ~90% did not develop TB after immunosuppression following untreated HIV or in preparation for organ or stem cell transplantation (reviewed in14). Self-clearance of Mtb infection is likely to have a substantial impact on TB epidemiology.15 Due to self-clearance, the population with a viable Mtb infection may be markedly smaller than generally assumed, with such individuals at greater risk of progressing to TB disease. Since the efficacy of new TB vaccines may depend on the host infection status, self-clearance also has the potential to affect the impact of new vaccines. While a number of more recent vaccine models have included a simple representation of self-clearance,6 8 none have investigated its effects on vaccine impact, or have explored the effects of the large amounts of uncertainty that exists around the properties of self-clearance. No earlier models included self-clearance at all.10 16 This neglect could result in biased estimates of vaccine impact, and potentially lead to suboptimal vaccine candidates being prioritised.
The aim of this work is to estimate the degree and direction of bias that may be resulting from the neglect of self-clearance in current models of TB vaccination.
Methods
Settings
Our analysis focuses on China and India. Conducting our modelling work on these countries allows us to evaluate how self-clearance may affect the potential impact of new vaccines in two countries that are key for the elimination of TB, and with different demographic and epidemiological characteristics. China and India are both high TB burden countries, accounting for about one-third of the total global TB deaths in 2021.1 The epidemiology of TB and the demography differ in these two settings. India has a younger population, with a birth rate almost three times higher than China. Furthermore, China has experienced a more substantial reduction in TB incidence over the past few decades,17 with current estimates of incidence being 3–5 fold lower in China than in India.
Model and self-clearance structure
To estimate how self-clearance might affect the impact of novel TB vaccines, we adapted a compartmental age stratified dynamic Mtb transmission model that did include self clearance.8 Ten compartments constituted the TB natural history, allowing for Mtb infection along a spectrum from uninfected to active clinical disease. Of these, five compartments referred to individuals with previous exposure to Mtb but without active disease:
Infection-Fast
: individuals with a recent infection with Mtb (less than 2 years).Infection-Slow-Early
: individuals last infected with Mtb in between two and 9 years before, who have not cleared their infection.Infection-Slow-Late
: individuals last infected with Mtb more than 9 years before, who have not cleared their infection.Uninfected-Cleared-Early
: individuals last infected with Mtb in between 2 and 9 years before, who have self-cleared their infection.Uninfected-Cleared-Late
: individuals last infected with Mtb more than 9 years before,who have self-cleared their infection.
Figure 1 illustrates the flows into and from the Uninfected-Cleared compartments. The main difference between our model and the one in8 is that we split the Uninfected-Cleared and Infection-Slow compartments into early (mean duration 7 years, and following a mean duration of 2 years in the Infection-Fast compartment) and late (starting an average of 9 years following last infection). This allowed us to calibrate the model to empirical data on the degree of protection against reinfection, which came from cohort studies where the follow-up period ended an estimated mean of 9 years following infection.18 This was done by varying the degree of protection against reinfection in people infected with Mtb, and assuming different levels of protection in people who had self-cleared (see the ‘Effect of different levels of natural protection in self-cleared individuals on vaccine impact’ section).
Diagram of the model structure showing parts of TB natural history related to infection and self-clearance in detail. : rate of self-clearance from Infection-Slow Early to Uninfected-Cleared Late. The full model structure showing the disease and treatment compartments, is shown in Supplementary Material, Section A. TB, tuberculosis.
The properties of self-clearance were specified by the following two parameters in the natural history: the rate 
at which individuals in Infection-Slow compartments self-cleared their infection, and the level of natural protection against reinfection that self-cleared individuals had, assumed to be 
times the natural protection against reinfection for individuals in the Infection-Slow compartments 
. Note that, following estimates from,15 the rate of self-clearance from the Infection-Fast compartment was set to 
in all scenarios.
For details on the full model structure, please see online supplemental section A.
Supplemental material
Vaccines
It is uncertain whether new TB vaccines will only work in uninfected people (NCI vaccines), in people already infected with Mtb (CI vaccines), or in both (any infection vaccines, AI). Similarly, it is uncertain whether new TB vaccines will act to reduce the risk of infection (prevention of infection, POI), disease progression (prevention of disease, POD) or both (prevention of infection and disease, POID). We explored the impact of six different potential vaccines, consisting of the plausible combinations of the two characteristics (table 1). A priori, we would expect that the inclusion of self-clearance in models will have limited effect on the impact of AI vaccines, as these vaccines have the same impact in all individuals, regardless of their infection status. We, therefore, focus mainly on NCI and CI vaccines in the results of this paper, and only consider the most extreme self-clearance scenarios for AI vaccines (see the ‘Maximum potential effect of including self-clearance in models of TB vaccination’ section).
Characteristics of the six vaccines implemented
Each vaccine was assumed to have a maximum efficacy of 50%, and was implemented with a one-time mass campaign for people aged 10 or older in 2025, and routine vaccination of 10 years old from 2025 to 2050. For the POID vaccine, an efficacy of 50% meant that both the force of infection and the rates of progression to Subclinical-Disease were halved. Both the mass campaign and the routine vaccinations were assumed to have a 70% coverage. Estimated vaccine impact was calculated as the TB incidence rate reduction in 2050 compared with a scenario where no new vaccine was implemented.
For more details on the implementation of the vaccine, see online supplemental section C.
Self-clearance scenarios
In our central self-clearance scenario, we assumed a self-clearance rate equal to the median value provided in15 (
= 0.033/year), a level of natural protection against reinfection in Uninfected-Cleared compartments equal to half the protection in the Infection-Slow compartments 
, and a vaccine efficacy in self-cleared individuals equal to 25%, that is, half the maximum efficacy assumed. To account for the uncertainty that exists in these values, we created a number of scenarios, varying them individually and in combination.
Effect of different self-clearance rates on vaccine impact
To estimate the effect that different self-clearance rates may have on vaccine impact, we compared the scenario with no self-clearance with three scenarios where the self-clearance rate was set to the 2.5th percentile, the median and the 97.5th percentile provided in.15 The other two characteristics, that is, level of natural protection and vaccine efficacy in self-cleared individuals, were set to their mid-values.
Effect of different levels of natural protection in self-cleared individuals on vaccine impact
To estimate the effect that different levels of natural protection against reinfection in self-cleared individuals may have on vaccine impact, we compared the scenario with no self-clearance with three scenarios where the level of natural protection against reinfection in the Uninfected-Cleared compartments was set to 0%, 50% or 100% of the protection against reinfection in the Infection-Slow compartments. The other two characteristics, that is, self-clearance rate and vaccine efficacy in self-cleared individuals, were set to their mid-values.
Effect of different levels of vaccine efficacy in self-cleared individuals on vaccine impact
To estimate the effect that different levels of vaccine efficacy in self-cleared individuals may have on vaccine impact, we compared the scenario with no self-clearance with three scenarios where the vaccine efficacy in self-cleared individuals was set to 0%, 25% and 50%. The other two characteristics, that is, self-clearance rate and level of natural protection in self-cleared individuals, were set to their mid-values.
Maximum potential effect of including self-clearance in models of TB vaccination
To estimate the maximum effect that the inclusion of self-clearance in models may have on vaccine impact, we compared the scenario with no self-clearance with eight scenarios, corresponding to all possible combinations of the extreme values (minimum and maximum) of the three self-clearance characteristics (self-clearance rate, level of natural protection and vaccine efficacy in self-cleared individuals).
Furthermore, since the work in15 was intended to provide a robust lower-bound of the self-clearance rate, we also estimated the maximum effect that self-clearance may have on vaccine impact if self-clearance rates higher than in15 are considered. As the model was calibrated to the proportion of infected individuals, there is an upper bound on the proportion of all people who can be in the Uninfected-Cleared compartment. Therefore, we were able to generate a crude estimate of the maximum impact by extrapolating from the results of the low, medium and high self-clearance rate scenarios (see online supplemental section D for full details).
Calibration process
For each country and each self-clearance scenario (with the exception of scenarios varying the vaccine efficacy in self-cleared people, where the baseline fit does not change), we recalibrated the model to same epidemiological data using history matching with emulation using the hmer R package,19 20 generating at least 1000 fitted parameter sets. Results are presented as the median and 95% plausible range, calculated as the 2.5 and 97.5 percentile across the 1000+ available parameter sets.
The model for each country and scenario was calibrated to 9–12 calibration targets in 2019, including the TB case notification rate (overall and by age), TB mortality rate (overall), and the proportion of prevalent TB that is subclinical. We also calibrated the model to data on the prevalence of latent Mtb infection in individuals ages 15–99 (in 2000 for China and 2021 in India). To ensure that the evolution of the TB epidemic was captured, we matched to time trends in the TB disease prevalence in China (1990 and 2010), and to time trends in TB incidence in India (2000 and 2019). Finally, we matched to the estimate of the degree of protection against reinfection in latent individuals provided in.18 Full details on the calibration targets are given in online supplemental section B.
Patient and public involvement
Patients and/or the public were not involved in this study.
Results
Baseline scenarios
We successfully calibrated each self-clearance scenario for the two countries of interest, generating at least 1000 fits per scenario. Figure 2 shows how different values of the self-clearance rate and of the natural protection against reinfection in self-cleared individuals, affected the proportion of individuals in the various compartments, and the distribution of the flows into the Infection-Fast compartment in China in 2025. The corresponding plots for India are very similar and can be found in online supplemental section B. Since all individuals progressing to active disease need first to have passed through the Infection-Fast compartment, analysing how the flows into the Infection-Fast compartment vary across self-clearance scenarios will help us understand how incidence is affected by different parameterisations of self-clearance.
Proportion of individuals in each natural history state and distribution of flows into Infection-Fast in 2025 for different baseline scenarios in China, averaged across model runs. In scenarios with self-clearance, when the rate of self-clearance or the level of protection in Uninfected-Cleared individuals are not indicated, they are fixed at their mid value.
As expected, we see that higher self-clearance rates result in higher proportions of self-cleared individuals. On the other hand, varying the level of natural protection against reinfection in self-cleared individuals does not substantially change the proportion of individuals in the various compartments. There are three types of flows into the Infection-Fast compartment: from the Infection-Slow compartments, from the Uninfected-Cleared compartments and from the Uninfected-Naive compartment. In both countries, the size of the flow from Uninfected-Cleared compartments is slightly higher when the self-clearance rate is higher and substantially lower when the natural protection against reinfection in Uninfected-Cleared individuals is lower. Furthermore, the proportion of all flows into the Infection-Fast compartment that come from the Uninfected-Cleared compartments is always below 15% in both countries: this suggests that people who are self-cleared cannot directly affect more than 15% of the TB incidence.
Effect of self-clearance on vaccine impact (central scenario)
In the no-self-clearance scenario and central scenarios respectively, the no current infection, POI vaccine resulted in a relative disease incidence reduction in 2050 of 35.3% and 38.5% in China and 29.1% and 30.1% in India; the no current infection, OID vaccine in a reduction of 46.5% and 50.7% in China and 39.7% and 41% in India; the current infection, POD vaccine in a reduction of 27.9% and 27.5% in China and 25.2% and 25.8% in India; and the current infection, POID vaccine in a reduction of 29.2% and 29.2% in China and 26.2% and 26.4% in India (figure 3). Overall, in both countries, the largest relative disease incidence reduction was observed for no current infection vaccines: this is in accordance with the fact that the flow into Infection Fast coming from uninfected individuals (on whom no current infection vaccines are effective) was substantially larger than the flow coming from infected individuals (on whom current infection vaccines are effective) (figure 2).
Relative disease incidence reduction (%) in 2050 compared to a scenario with no vaccination, when each of the three self-clearance characteristics is varied separately for no current infection vaccines and current infection vaccines (China on the left, India on the right). For each plot, the two unshown characteristics are fixed to their mid value. The height of the columns show the median percentage reduction in incidence in 2050 compared to the scenario where no vaccine is implemented, with vertical bars indicating the 95% confidence interval. The numbers on top of each column correspond to the relative change in disease incidence reduction compared to the no self-clearance scenario. NCI: no current infection, CI: current infection, POI: prevention of infection, POD: prevention of disease, POID: prevention of infection and disease. CI, current infection; NCI, no CI; POD, prevention of disease; POI, prevention of infection; POID, POI and disease.
Effect on vaccine impact of different self-clearance rates
Figure 3 shows how the estimated relative reduction in TB incidence in 2050, compared with the baseline no vaccination scenario, varies when varying the three self-clearance characteristics (self-clearance rate, level of natural protection and vaccine efficacy in self-cleared individuals) separately. With the exception of the different vaccine efficacy scenarios, the model was calibrated separately to each scenario. Minor differences between scenarios in estimated vaccine impact may therefore reflect minor, chance differences between the baseline fitted scenarios, and not genuine differences in vaccine impact, particularly where trends are not observed.
Varying the self-clearance rate has only a small effect on estimated vaccine impact, for any vaccine in either country. The maximum relative increase in vaccine impact compared with the no self-clearance scenario when only varying the self-clearance rate, was 9% in China and 4% in India, while the maximum relative decrease was 3% in China and 1% in India. This is in accordance with figure 2: since the distribution of the flows into Infection-Fast changes little with the value of the self-clearance rate, we would not expect the impact of vaccines on TB incidence to be strongly affected by the rate of self-clearance.
Effect of different levels of natural protection in self-cleared individuals on vaccine impact
The maximum relative increase in vaccine impact when varying the level of natural protection against reinfection in self-cleared individuals, was 9% in both countries, while the maximum relative decrease was 8% in China and 7% in India. Increasing the level of natural protection against reinfection in self-cleared individuals decreased the impact of current infection vaccines. This is because as the level of protection was increased, the proportion of the flow into Infection-Fast that comes from the Self-Cleared compartments decreased (from 12.7% to 2.8% in China, and from 12.9% to 4.2% in India), and the proportion that comes from Uninfected-Naive increased (from 84.3% to 94.3% in China, and from 80.4% to 91.6% in India) (figure 2). As we assumed that current infection vaccines offered partial protection to self-cleared individuals, and none to uninfected individuals, this resulted in a lower estimated vaccine impact in scenarios with a higher level of natural protection in self-cleared individuals.
The results do not show the opposite trend for no current infection vaccines: an increase in vaccine impact as we increase the level of natural protection. The reason for this is that the relative change in the percentage of the flow into Infection-Fast coming from individuals in which the vaccines are effective is small (from 90.6% to 95.6% in China, and from 88.4% to 93.7%), and the effect on vaccine impact is therefore masked by minor differences between the calibrated baseline scenarios.
Effect of different levels of vaccine efficacy in self-cleared individuals on vaccine impact
The maximum relative increase in vaccine impact when varying the level of vaccine efficacy in self-cleared individuals was 11% China and 5% in India, while the maximum relative decrease was 5% in China and 3% in India. As expected, increasing the vaccine efficacy in self-cleared individuals, increases the estimated vaccine impact for all vaccine types. The increase in estimated impact for no current infection vaccines was substantially higher in China, compared with India. The main reason for this is to be found in the demographic differences between the two countries. Since only a very small percentage of 10 years old are in the self-cleared state (less than 1% both in China and India), self-clearance has a larger effect on the mass vaccination campaign than the routine vaccination of children. The effects of the mass vaccination are diluted much more quickly in India than China, due to the much higher birth rate, explaining the difference observed in figure 3 third row.
Maximum potential effect of including self-clearance in models of TB vaccination
Figure 4 shows the maximum relative decrease and increase in TB incidence reduction in 2050 obtained by exploring all possible combinations of the extreme values (minimum and maximum) of the three self-clearance characteristics (self-clearance rate, level of natural protection and vaccine efficacy in self-cleared individuals). The maximum relative increase for no current infection vaccines in our model was 12% in China and 8% in India. For current infection vaccines, the maximum relative increase was 12% in China and 15% in India, while the maximum relative decrease was 14% in China and 11% in India. Unsurprisingly, including self-clearance in the model only increased the impact of no current infection vaccines, as it increased the number of people in which vaccines work from people who have never been infected at the time of vaccination only, to also include people who have self-cleared (figure 2, first row). By the same logic, we may suppose that including self-clearance could only decrease the impact of current infection vaccines, however that is not what we observe: modelling self-clearance increases estimated vaccine impact in a number of scenarios, with the greatest increases found where self-cleared people have no protection against reinfection. The main reason is that a higher proportion of the flow into the Infection-Fast compartment comes from compartments where current infection vaccines can offer some protection (ie, the Infection-Slow and self-cleared compartments) in scenarios where we assume that self-cleared people have reduced or no protection against reinfection, compared with scenarios with no self-clearance (figure 2, second row). Additionally, 26%–35% of the vaccine doses are delivered through routine vaccination of 10 years old in the model, a group in which the prevalence of latent infection is 4–10 fold higher than the prevalence of self-cleared infection, even in the highest self-clearance rate scenarios.
Relative disease incidence reduction (%) in 2050 compared to a scenario with no vaccination, obtained by exploring all possible combinations of the extreme values (minimum and maximum) of the three self-clearance characteristics (self-clearance rate, level of natural protection and vaccine efficacy in self-cleared individuals), for no current infection vaccines and current infection vaccines (China on the left, India on the right). The height of the columns show the median percentage reduction in incidence in 2050 compared to the scenario where no vaccine is implemented, with vertical bars indicating the 95% confidence interval. The numbers on top of each column correspond to the relative change in disease incidence reduction compared to the no self-clearance scenario. For no current infection vaccines, the yellow columns are missing in China, as no explored scenario resulted in a decreased vaccine impact. NCI: no current infection, CI: current infection, POI: prevention of infection, POD: prevention of disease, POID: prevention of infection and disease. CI, current infection; NCI, no CI; POD, prevention of disease; POI, prevention of infection; POID, POI and disease.
See online supplemental section D, for details on the specific scenarios in which each maximum decrease and increase were achieved for each vaccine.
As the empirical estimate of self-clearance rates used in the model was designed to be a plausible lower bound,15 we also estimated the maximum potential effect of self-clearance on vaccine impact for higher self-clearance rates. Even when assuming that self-clearance occurs instantly from the Infection states, the estimated maximum relative increase for the impact of no current infection vaccines was never higher than 16%, while the estimated maximum relative increase/decrease for the impact of current infection vaccines was never higher than 25% (table 2) (for the complete analysis, see online supplemental section D).
Upper bounds on the maximum increase and decrease in disease incidence reduction in 2050, compared with the no self-clearance scenario, that can result when allowing self-clearance rates higher than those provided in Emery et al15
Effect of self-clearance on the impact of AI vaccines
As hypothesised, the inclusion of self-clearance has little effect on the impact of AI vaccines, with the disease incidence reduction in 2050 being increased by a maximum of 6% in China and 3% in India, relative to the scenario with no self-clearance (see online supplemental section E for full results). These small changes are likely to result from chance differences between the baseline fitted scenarios, and not genuine differences in vaccine impact.
Discussion
Our results suggest that the neglect of self-clearance in mathematical models of TB is not leading to substantial bias in estimates of vaccine impact, with maximum relative increases or decreases in impact of up to 15% only. As expected, modelling self-clearance had little effect on the estimated impact of AI vaccines, as these vaccines are assumed to work in all people, regardless of infection status. Not including self-clearance in models may mean that we are slightly underestimating the impact of no current infection vaccines, slightly underestimating or overestimating the impact of current infection vaccines, and slightly underestimating the increased impact of no current infection vaccines compared with current infection vaccines. Underestimation of the impact of no current infection vaccines is likely to be higher for vaccine roll-out scenarios that target older age groups, compared with children or adolescents only, and for countries with lower birth rates.
This paper presents some of the first work exploring how the inclusion of self-clearance in mathematical models affects estimates of intervention impact. We demonstrate that self-clearance does not substantially affect estimates of vaccine impact, however, it is still to be investigated how the neglect of self-clearance in TB models may affect estimates of impact for other interventions, (eg, preventative treatment or different screening strategies). Based on our analysis, a relatively small proportion of incident disease occurs in (previously) self-cleared individuals, in two high burden countries with very different demographic and epidemiological characteristics. Taking this into consideration, it seems unlikely that neglecting self-clearance in TB models would lead to substantially biased estimates of the impact of interventions targeting the overall population. The impact of self-clearance might, however, be higher for interventions that target or only work in subgroups with a substantially larger proportion of self-cleared individuals than in the entire population.
This work could be used to support implementation policy. At its simplest level, it could be used to justify the use of evidence from TB vaccine models that do not incorporate self-clearance when making TB vaccine estimates for overall populations. At a more detailed level, it could be used to approximate the direction and potential magnitude of errors in estimates for general populations using models that do not incorporate self-clearance (although that there will be many other sources of error), including making decision-makers aware that we may be underestimating the relative advantages of vaccines that work in uninfected individuals only, compared with those that work in infected individuals. For impact estimates on target populations in which the prevalence of self-clearance is expected to be higher than assumed here, it could be preferable for decision-makers to use estimates from models that incorporate self-clearance.
It should be noted that the aim of this work was to explore the effect of including self-clearance in models of vaccine impact, not to obtain estimates of vaccine impact in differing vaccine and implementation scenarios. In all implemented scenarios, we assumed that vaccines would confer lifelong protection (against infection and/or progression to disease) and that they were delivered with a mass campaign for people aged 10 or more, followed by the routine vaccination of 10 years old. These assumptions were made in order to maximise the potential vaccine impact, and the effect of including self-clearance on vaccine impact. Similarly, we did not include some details in the model that could have improved estimates of vaccine impact, but had little effect on how modelling self-clearance altered impact (eg, differences in treatment mortality and completion probabilities between the public and private sector in India).
We chose to model China and India partly because they have very different demographical and epidemiological characteristics. We, therefore, believe that the overall finding that the neglect of self-clearance in models of TB does not substantially bias estimates of vaccine impact in likely to be true across a wide range of settings. The exception to this may be countries where HIV plays an important role in the epidemiology of TB, for example, South-Africa. In this paper, we chose not to model any such country, because of the even greater levels of uncertainty around characteristics of self-clearance of Mtb infection in people living with HIV. However, it seems plausible to assume that living with HIV would reduce people’s ability to self-clear their Mtb infection, which suggests that neglecting self-clearance in models of countries with HIV driven TB epidemics may also not be resulting in substantially biased estimates of vaccine impact.
There are a number of limitations to our work. First, in our main analysis, we modelled self-clearance rates according to the estimates in Emery et al,15 which were meant to provide a robust lower bound for the self-clearance rate. To account for the fact that self-clearance rates may be higher than these estimates, we also explored higher self-clearance rates through statistical estimation of their potential values. We showed that the estimated maximum relative increase in vaccine impact compared with the no self-clearance scenario for no current infection vaccines was never higher than 16%, and the estimated maximum relative increase/decrease for current infection vaccines was never higher than 25%. These values strengthen the message that neglecting self-clearance does not lead to substantially biased estimates of vaccine impact. Another limitation is that modelling different baseline scenarios inevitably leads to chance differences between scenarios. These differences are minor only and are unlikely to have had any real impact on our conclusions, however they may have masked minor differences between scenarios.
TB natural history, and how best to represent it in mathematical models, is an area of active research. Future work may indicate that other changes to model structure could improve our representation of TB. For instance, modifications may be necessary if empirical data suggest that vaccine efficacy varies between people who have never been exposed to Mtb, and people who have been exposed without developing a detectable immune response. Changes in how these aspects of natural history are simulated could lead to higher or lower estimates of vaccine efficacy.
In conclusion, our work suggests that the neglect of self-clearance in mathematical models of TB vaccines does not result in substantially biased estimates of TB vaccine impact. It may, however, mean that we are slightly underestimating the relative advantages of vaccines that work in uninfected individuals only compared with those that work in infected individuals.
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information.
Ethics statements
Patient consent for publication
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
RW and NM are joint senior authors.
Handling editor Alberto L Garcia-Basteiro
Twitter @ra_clark18, @cweerasuriya, @@richardwhite321
Contributors DS: conceptualisation; data curation; formal analysis; methodology; Software; visualisation; writing—original draft; writing—review and editing. RAC: conceptualisation; data curation; software; writing—review and editing CKW: conceptualisation; writing—review and editing JE conceptualisation; writing—review and editing. RMGJH: conceptualisation; writing—review and editing. RW: conceptualisation; funding acquisition; methodology; supervision; writing—review and editing. NM: conceptualisation; funding acquisition; methodology; formal analysis; supervision; writing—review and editing. Guarantor: DS.
Funding This work was funded by Wellcome Trust (218261/Z/19/Z) and WHO (2020/985800-0). RW is funded by the Wellcome Trust (218261/Z/19/Z), NIH (1R01AI147321-01), EDTCP (RIA208D-2505B), UK MRC (CCF17-7779 via SET Bloomsbury), ESRC (ES/P008011/1), BMGF (INV-004737, INV-035506) and the WHO (2020/985800-0).
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.