Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis

Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-022-02378-1.

For PLHIV, as illustrated in Additional file 1 FigS1, we assumed that amongst those initiating ART, a proportion undergoes preventive treatment. We modelled the full implementation of WHO guidelines in this population by increasing from its current country-specific value (obtained from WHO data) to 1, over a period of three years, and maintained at this level thereafter.
Modelling household contacts presents some challenges, as compartmental models do not lend themselves to modelling of household structure [25,26], and studies aiming to capture this structure typically employ more complex, individual-based approaches instead [27].
However, compartmental models have the advantage of simplicity and ease-of-calibration. We developed the following approach for modelling preventive treatment amongst household contacts within compartmental models.
Our approach rests on the fact that household contacts with TB infection are at greater risk of developing active disease than those infected in the general population, being more likely to have arisen from recent infection. For example, a study involving longitudinal follow up of household contacts of diagnosed TB cases showed that the TB incidence in the cohort of contacts was 8 times greater that of the general population [23].
Here FigS3 shows how this feature of close contacts could be captured within a simple compartmental representation of TB natural history, extracted from the overall model structure shown in Fig 1 in the min text. It is assumed that TB infection can be divided into an early 'high risk' stage (Lf for 'fast progressors') and a subsequent 'low risk' stage (Ls for 'slow progressors'), with individuals progressing from the first to the second if they do not develop TB disease. A recent systematic review showed how this structure is consistent with available epidemiological data from the pre-chemotherapy era [10]. Because this model does not explicitly incorporate household structure, the number in each of these compartments at a given point in time should be interpreted as representing population-level prevalence in each of the different states.
The vertical, dashed arrows illustrate the uptake of preventive therapy in this population, amongst household contacts of TB cases. Because TB-infected household contacts are more likely to have had recent infection than those in the general community, there would be an over-representation of individuals from the 'latent fast' (HH (Lf) ) compartment: that is, we have > 1. If the incidence of TB amongst household contacts is (say) k times that of the general population, neglecting for now the effect of preventive treatment on incidence, we have that: where Δ is a one-year time interval. For a given k and (in Fig.S3), it is possible to satisfy this equation by adjusting appropriately (in Fig.S3). It remains to determine . To do so, we note that the total number of household contacts initiated on preventive therapy in a given year is: where N is the number of individuals with TB notified in a given year, and hs is the average household size (table S3). Given country-specific estimates for both of these parameters, [equation 26] therefore supplies a criterion for to satisfy.
To implement this approach, we took as initial conditions the state values of the calibrated model simulated at 2020 (i.e. prior to the intervention of scaling up preventive treatment). For given values of , , we then simulated the model forward by an interval of Δ = 1 year, assuming preventive therapy to have no efficacy. Based on the model outputs, we then used simple least-squares optimisation to determine the values of , satisfying equations 26 and 27 simultaneously. For all subsequent years we held constant, while allowing to vary in proportion to annual notifications.
The output of this process is to identify a value for , and an annual timeseries for . To model the impact of preventive therapy amongst household contacts, we then simulated the model dynamics on incorporating , , at the same time as allowing a non-zero efficacy for preventive treatment.