Keywords
Trachoma, Elimination as a public health problem, mass drug administration, surveillance, monitoring and evaluation
This article is included in the 2030 goals for neglected tropical diseases collection.
Trachoma, Elimination as a public health problem, mass drug administration, surveillance, monitoring and evaluation
The views expressed in this article are those of the author(s). The opinions expressed herein are those of the authors and do not necessarily reflect the views of the World Health Organization. Publication in Gates Open Research does not imply endorsement by the Gates Foundation.
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 blindness1.
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 20201, 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 efforts2, 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 consortium3. 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 level4.
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 infections5), 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-term4,6–8. More intensive facial cleanliness and environmental improvement (F&E) or more intensive antibiotics are measures that will be necessary in a select few hot spots2,6,9. 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 20204,6. 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 transmission2,10. 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 infection11.
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 dose12. 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 communities13. 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 settings14. This modelling suggested that sustained F&E could help maintain the gains initially achieved through intense antibiotic distribution14. 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 (TESFA16) are due to be trialled.
The current monitoring and evaluation survey design has been useful to predict large-scale trends and to estimate evaluation unit (EU) level prevalence of TF (as intended)1. However, as TF prevalence continues to decline the sensitivity and specificity of the eye examination may also decline. As fewer cases are available to train graders and the severity of the cases decreases making them harder to confirm. Equally, as prevalence decreases, noise due to sampling variation increases17. Complete cost-effective modelling work is yet to be published, but using TF surveillance for the current end goal is becoming more expensive. Additionally, recent epidemiological studies in the South Pacific have highlighted that TF is apparent within communities in the absence of being able to identify C. trachomatis through PCR18. This has led the community to start considering whether evaluation by PCR or through serology may be more appropriate as prevalence continues to decline19,20. However, to date limited data with all three diagnostics where TF is ~5% have been available to understand if or how all diagnostic indicators relate to each other at low prevalence. Some recent modelling sought to evaluate the relationship between TF and serological prevalence21; however, more data are needed to test the robustness of these findings. Collectively, current modelling and surveillance data suggest that as we move towards 2030 the target (currently TF prevalence) measured may need to vary by region and be tailored to the underlying epidemiology of certain areas.
Trials and longitudinal studies have found that after MDA, infection can return7,11,22, however these were locations where TF prevalence had not declined to <5%11,22. It has been suggested that infection could re-emerge due to the loss of age-specific immunity as transmission reduces23, however to date no re-emergence has been detected in districts that have eliminated trachoma. Since TF prevalence is a lagging indicator, TF-driven programmatic activities may continue long enough to frequently achieve near elimination of C. trachomatis infection. PCR as an alternative indicator for detecting resurgent infection has a number of problems, not least the short duration of infection17. Moreover, it can be fairly costly and requires specialized equipment and technicians, therefore it can be challenging to implement, albeit capacities in many trachoma-endemic countries are improving. Nevertheless, demonstrating that the causative agent of infection is absent in endemic or formally endemic communities is the key indicator of breaking transmission. In the absence of dedicated post-elimination TF prevalence surveys, serological studies may be able to detect substantial resurgence in transmission despite imperfect antibody specificity21.
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 goal3,9,24. 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 field25. 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 treated26. 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 undermined3. 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 EPHP21,27,28. 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.
As prevalence and transmission of trachoma declines, the specificity of TF as a diagnostic indicator of conjunctival CT infection is also reported to decline28,29. Equally, following elimination of TF there is likely to be limited funding dedicated to TF surveillance to monitor and verify elimination. Therefore, it will be important to understand what alternative diagnostics can tell us about transmission of trachoma, one such diagnostic being the use of serology.
If serology is informative, the opportunity for trachoma post-validation surveillance increases as dried blood spots collected for other health programs have the potential to be used to also test for a response to trachoma antigens30–32. As such, although not specifically within the 2030 targets, research into the utility of sero-surveillance for understanding and quantifying transmission is of importance for trachoma elimination. A number of individual modelling analyses have been conducted to try and estimate sero-conversion rates (SCRs) for trachoma within different settings30,31,33. However, individual modelling analyses of datasets in isolation make it difficult to understand the global picture. A more recent modelling analysis collated datasets with TF and sero- surveillance from a number of endemic regions21 which estimated the SCRs from multiple different study sites and correlated these with the reported TF prevalence. This work was the first attempt to estimate an operational threshold for serology for trachoma programs. Modelling suggested that SCRs below 0.015 (95% confidence interval (CI): 0.0–0.049) per year corresponded to a prevalence of TF below 5%, the current threshold for elimination of active trachoma as a public health problem21. Additionally, in terms of understanding the operational feasibility of conducting sero-surveys for trachoma, a statistical analysis suggested that sero-surveillance would require smaller sample sizes for any given level of prevalence than TF because the level of sero-prevalence that programs would be measuring is higher than the TF prevalence21.
It is also important to highlight that results from this modelling study suggest that further work is required before serology can be recommended as a post-validation surveillance tool. One existing limitation is that current analyses are being done using both bead-based multiplex immunoassay systems, ELISA and lateral flow assays; standardization would aid comparison between sites. Additionally, it is unclear exactly what the population-level serological profile looks like in areas with sustained subcritical transmission of ocular C. trachomatis. A greater understanding of this is required before one can interpret serological data for trachoma in the context of post-validation surveillance.
In discussion with WHO, a number of priority issues and questions for trachoma control programs were identified. These questions are summarized in Table 2 and describe how mathematical and statistical modelling can potentially help address them.
Priority issue/question identified in discussion with WHO | How can modelling address this? |
---|---|
Forecasting expected timeline to reach the goals | Probabilistic forecasts can be developed using statistical and mechanism-based models. These forecasts must obviously be taken with caution, understanding the assumptions made and the uncertainty in the outcomes predicted. |
How likely/unlikely is resurgence, how quickly is it likely to emerge and be detected and where is it more likely to emerge? | One approach is to analyse data from districts that return to TF prevalence >5% and assess the likelihood of true resurgence versus misclassification error, and compare it with outputs from resurgence in stochastic models. Our group has been working on this, using data collected by Trachoma endemic countries and adapting the population-based deterministic model in 17 to be stochastic (unpublished). To better understand timeliness of resurgence and where it is more likely to occur, scenario- based simulations could be potentially used, but eventually a more spatially explicit model would be needed. To inform such a model, a review of empirical studies is required, which can help inform changes in endemic areas. These models would have to include diagnostics in an explicit manner, so that surveillance approaches and detection of resurgence can be appropriately assessed. |
A geospatial survey design for TT | To produce a geospatial survey design, geostatistical models can be used that can account for both spatial and temporal uncertainty in the TT estimates. This will improve survey design and will lead to a better understanding of the needs at fine geographical scales. However, this approach requires spatially explicit data. |
What is the utility of serology in Identification of current hot spots and future resurgence | Modelling work has been carried out to analyse whether serological data is informative of patterns of transmission and whether it could be used, as global prevalence declines, as a tool to inform programmatic decisions21. More serological data will be available in the future that can be integrated to models already developed to tackle the identification of potential hotspots. |
No data are associated with this article.
Members of NTD Modelling Consortium discussion group on trachoma are, in alphabetical order:
Benjamin F. Arnold1, Robin L. Bailey2, Michael Deiner1, William Godwin1, T. Deirdre Hollingsworth3,*, Thomas M. Lietman1,*, Amy Pinsent2, Travis C. Porco1, Joaquin M. Prada4, Michelle Stanton5
1 Francis I Proctor Foundation, University of California, San Francisco, CA 94143, United States
2 The Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
3 Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
4 School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK
5 Centre for Health Informatics, Computing and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster LA1 4YW, UK
*Corresponding authors
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Is the rationale for the Open Letter provided in sufficient detail?
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Does the article adequately reference differing views and opinions?
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Are all factual statements correct, and are statements and arguments made adequately supported by citations?
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References
1. Mabey D, Solomon A, Foster A: Trachoma. The Lancet. 2003; 362 (9379): 223-229 Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: trachoma epidemiology
Is the rationale for the Open Letter provided in sufficient detail?
Yes
Does the article adequately reference differing views and opinions?
Yes
Are all factual statements correct, and are statements and arguments made adequately supported by citations?
Partly
Is the Open Letter written in accessible language?
Yes
Where applicable, are recommendations and next steps explained clearly for others to follow?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Infectious Disease Epidemiology
Alongside their report, reviewers assign a status to the article:
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Version 1 03 Dec 19 |
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