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Trachoma is a neglected tropical disease caused by infection with the bacterium Chlamydia trachomatis. During an infection episode, conjunctival inflammation occurs, which leads to the presence of follicles on the eyelids (active trachoma, Trachomatous inflammation-follicular (TF)) ¹ . Repeated infection with the bacteria over time, which results in scarring of the eyelids, leading to in-turning of the eyelashes, known as trachomatous trichiasis (TT), which traumatizes the eye surface leading to superinfection and blindness ¹ .

The World Health Organization leads an Alliance that aims to achieve the elimination of trachoma as a public health problem (EPHP) in all endemic districts by 2020 ¹ , this is defined by the achievement of three goals: 1) reduction of TF prevalence in 1–9 year olds to <5% 2 years after mass drug administration (MDA) interventions have halted, 2) a TT prevalence unknown to the health system in >=15-year-olds of <0.2% , and 3) the presence of a system to identify and manage incident cases of TT. In order to eliminate trachoma, the WHO endorses the implementation of the SAFE strategy which consists of four components: (S) surgery to correct trichiasis; (A) mass distribution of antibiotics to clear infection in the community (topical tetracycline is used in very young children or other individuals unable to take azithromycin), (F) promotion of facial cleanliness in order to reduce transmission via eye discharge and (E) environmental improvement to ensure that the environment no longer helps to facilitate the transmission of infection. MDA is provided to all districts where TF is >5%. A course of three annual rounds is recommended to all regions where TF is between 10–30%, after which a follow-up survey is conducted to assess whether further rounds of MDA are required. To date, 9 countries have been validated by WHO as having achieved EPHP.

Both mathematical and statistical models have been developed to gain insight into the transmission dynamics of infection. Such models have been used to try and understand the potential impact of different intervention strategies that could help to accelerate elimination efforts ² , as well as understanding likely elimination timelines through forecasting. In addition, a recent review on the contribution of mathematical modelling to trachoma research and elimination efforts was published by the two teams in the first iteration of the NTD modelling consortium ³ . Furthermore, a multi-group forecast comparison was also conducted to look at the strengths and limitations of different modelling approaches for forecasting the future prevalence of TF at the district level ⁴ .

Moving forward past the current 2020 goals, whilst substantial progress has been made towards achieving EPHP of trachoma, it has become apparent that a number of endemic regions will not achieve this target by 2020. Therefore, WHO is planning to revise the timeline, with the aim of achieving EPHP in all endemic districts by 2030. Using the insights that have been gained from recent modelling work on trachoma, in this article we highlight the practical implications of EPHP (the timelines required, sufficiency of current surveillance diagnostics and feasibility of achieving it) and the future considerations that may be needed following EPHP to maintain the gains ( Table 1 provides a summary of the key issues).

There has been a substantial amount of programmatic success in trachoma elimination over the last 10 years, with global prevalence falling dramatically as a result of successful intervention programs. Whilst EPHP includes both TF and TT, most comments discussed in this article will primarily pertain to the prevalence of and the interventions that have been implemented against active trachoma (TF)/ocular Chlamydia trachomatis infection, and thus the article will focus on changes, modelling and monitoring of TF and infection prevalence. Limited analysis and forecasting of TT prevalence to date has occurred in part because the trajectory of changes in TT prevalence depends not only on the incidence of TT (a chronic condition and stochastic process which may not only be dependent on an individual’s past number of infections ⁵ ), but also due to demography and health service access – the prevalent number of TT cases is also determined by the speed and efficiency of active case finding and surgical service delivery, which are inherently more challenging and uncertain to model.

Mathematical modelling and current surveillance data suggests that EPHP is feasible, and indeed has already been achieved by a number of endemic countries. However, in areas with long-term persistence, such as a few high prevalence districts in Ethiopia (>40% baseline prevalence), MDA alone is not sufficient to achieve EPHP and must be supplemented with additional tools which helps reduce transmission of infection over the long-term ^{4, 6– 8} . More intensive facial cleanliness and environmental improvement (F&E) or more intensive antibiotics are measures that will be necessary in a select few hot spots ^{2, 6, 9} . Similarly, statistical analysis of data provided and collected by trachoma endemic countries has indicated that the vast majority of endemic evaluation units (EUs) are on track to achieve TF <5% for EPHP by 2020 ^{4, 6} . These findings are consistent across both dynamic and statistical modelling frameworks that were independently developed by the different partners of our consortium.

In areas that remain problematic, to understand how EPHP may be achieved by 2030, dynamic modelling work has explored a range of alternative and more intensive antibiotic distribution strategies that could be implemented, even in areas with the highest rates of transmission ^{2, 10} . To date it has been challenging to measure the true impact of F&E and its potential role in helping to reduce transmission, and thus it has been challenging to model, however an on-going clinical trial is seeking to help try and address this gap (Stronger SAFE). Although even if annual mass antibiotic treatment is insufficient to achieve EPHP goals in certain hyperendemic areas, it may prevent resurgence of infection ¹¹ .

Modelling has also been used to investigate whether targeting a residual core group of children with additional antibiotic treatment, while continuing annual MDA to the entire EU would be more effective at clearing infection from the community than implementing a single annual dose ¹² . The study suggested that if average duration of infection per group and dominant eigenvalue of a next generation matrix of the transmission model are defined, then a sufficient core group can be determined and used to find the absolute minimum sized core group, based on a fully specified model or even from epidemiological data. A number of RCTs are currently underway in Ethiopia with the design and hypotheses under investigation informed by modelling, with the aim of assessing the potential impact of alternative and intensive treatment strategies. One RCT (KETFO) is assessing whether quaterly treatment of children alone can lead to EPHP in severely affected communities ¹³ . Mathematical modelling of a double-dose antibiotic treatment strategy where two doses of antibiotics are given two weeks apart, in combination with enhanced F&E suggested that feasibility of EPHP may be increased in high transmission settings ¹⁴ . This modelling suggested that sustained F&E could help maintain the gains initially achieved through intense antibiotic distribution ¹⁴ . Additionally, two RCTs, one looking at intensive WASH (SWIFT-WUHA) ¹⁵ and the second looking at the distribution of two doses of antibiotics one week apart (TESFA ¹⁶ ) are due to be trialled.

There are a number of practical factors that may directly impact on-going program implementation that may need to be considered and mitigated against as programmes continue. Firstly, both empirical data and dynamic modelling have suggested that in areas of high prevalence, MDA alone is not sufficient to reach the goal ^{3, 9, 24} . As previously described, a number of alternative intervention strategies are currently being evaluated within RCTs to try and mitigate against this problem. Secondly, maintaining and optimising the frequency of antibiotic use is of paramount importance in order for gains to be achieved and maintained. Coverage is often reported to be high, but in practice this can be hard to measure in the field ²⁵ . Equally, systematic non-access, particularly amongst those who are also not included in surveys, may limit progress in reducing transmission by leaving reservoir sources of infection in communities that are deemed to have been treated ²⁶ . Thirdly, to date, no resistance to azithromycin has been reported, however careful monitoring for suboptimal treatment effects is needed because if resistance does emerge, EPHP success will be severely undermined ³ . Fourthly, as prevalence begins to decline in many endemic regions, movement of individuals between infected and uninfected areas may facilitate persistence of infection or re-introduction into formerly infection-free areas.

A number of risks remain for surveillance in terms of classifying and continuing to confirm elimination. First of all, it is currently uncertain whether or not TF prevalence is specific enough to classify EUs that have achieved EPHP ^{21, 27, 28} . Due to the non-linearity in the relationship between PCR detectable infection and TF at low-levels understanding how the two diagnostics relate to one another and truly reflect transmission can be challenging. Additionally, TF has been detected in some areas of the world without the bacterial organism being identified, suggesting that other factors besides trachoma may also cause TF. Equally, following validation, it is currently uncertain how to conduct surveillance to ensure that EPHP is maintained. Serology has been suggested as one potential option, although post-validation sero-surveillance data are only starting to become available now. Nevertheless, the current lack of post-validation strategy is a risk for the long-term success of the programs.

There are a number of risks that we need to be mindful of with respect to modelling trachoma and also interpreting the model outputs. In all modelling to date it has been assumed that our ability to detect TF remains the same and will be so in the future. However, this is an optimistic assumption, as we expect ability to recognize TF to decrease as the disease becomes rarer. Therefore, models are likely to need refining as we begin to focus on modelling surveillance in very low transmission scenarios. Importantly, there are no high-resolution empirical studies on dynamics of infection in areas with hypo-endemic disease, which means that simulations modelling low-level prevalence are likely to have a large number of uncertainties. Therefore, further empirical studies and modelling work are needed in order to understand how to more accurately model transmission at low prevalence.

No data are associated with this article.