Assessment of transmission in trachoma programs over time suggests no short-term loss of immunity

District-Level Forecast of Achieving Trachoma Elimination as a Public Health Problem By 2030: An Ensemble Modelling Approach

Assessing the feasibility of 2030 as a target date for global elimination of trachoma, and identification of districts that may require enhanced treatment to meet World Health Organization (WHO) elimination criteria by this date are key challenges in operational planning for trachoma programmes. Here we address these challenges by prospectively evaluating forecasting models of trachomatous inflammation-follicular (TF) prevalence, leveraging ensemble-based approaches. Seven candidate probabilistic models were developed to forecast district-wise TF prevalence in 11 760 districts, trained using district-level data on the population prevalence of TF in children aged 1-9 years from 2004 to 2022. Geographical location, history of mass drug administration treatment, and previously measured prevalence data were included in these models as key predictors. The best-performing models were included in an ensemble, using weights derived from their relative likelihood scores. To incorporate the inherent stochasticity of disease transmission and challenges of population-level surveillance, we forecasted probability distributions for the TF prevalence in each geographic district, rather than predicting a single value. Based on our probabilistic forecasts, 1.46% (95% confidence interval [CI]: 1.43-1.48%) of all districts in trachoma-endemic countries, equivalent to 172 districts, will exceed the 5% TF control threshold in 2030 with the current interventions. Global elimination of trachoma as a public health problem by 2030 may require enhanced intervention and/or surveillance of high-risk districts.

Mass azithromycin distribution for hyperendemic trachoma following a cluster-randomized trial: A continuation study of randomly reassigned subclusters (TANA II)

BACKGROUND

The World Health Organization recommends annual mass azithromycin administration in communities with at least 10% prevalence of trachomatous inflammation-follicular (TF) in children, with further treatment depending on reassessment after 3-5 years. However, the effect of stopping mass azithromycin distribution after multiple rounds of treatment is not well understood. Here, we report the results of a cluster-randomized trial where communities that had received 4 years of treatments were then randomized to continuation or discontinuation of treatment.

METHODS AND FINDINGS

In all, 48 communities with 3,938 children aged 0-9 years at baseline in northern Ethiopia had received 4 years of annual or twice yearly mass azithromycin distribution as part of the TANA I trial. We randomized these communities to either continuation or discontinuation of treatment. Individuals in the communities in the continuation arm were offered either annual or twice yearly distribution of a single directly observed dose of oral azithromycin. The primary outcome was community prevalence of ocular chlamydial infection in a random sample of children aged 0-9 years, 36 months after baseline. We also assessed the change from baseline to 36 months in ocular chlamydia prevalence within each arm. We compared 36-month ocular chlamydia prevalence in communities randomized to continuation versus discontinuation in a model adjusting for baseline ocular chlamydia prevalence. A secondary prespecified analysis assessed the rate of change over time in ocular chlamydia prevalence between arms. In the continuation arm, mean antibiotic coverage was greater than 90% at all time points. In the discontinuation arm, the mean prevalence of infection in children aged 0-9 years increased from 8.3% (95% CI 4.2% to 12.4%) at 0 months to 14.7% (95% CI 8.7% to 20.8%, P = 0.04) at 36 months. Ocular chlamydia prevalence in communities where mass azithromycin distribution was continued was 7.2% (95% CI 3.3% to 11.0%) at baseline and 6.6% (95% CI 1.1% to 12.0%, P = 0.64) at 36 months. The 36-month prevalence of ocular chlamydia was significantly lower in communities continuing treatment compared with those discontinuing treatment (P = 0.03). Limitations of the study include uncertain generalizability outside of trachoma hyperendemic regions.

CONCLUSIONS

In this study, ocular chlamydia infection rebounded after 4 years of periodic mass azithromycin distribution. Continued distributions did not completely eliminate infection in all communities or meet WHO control goals, although they did prevent resurgence.

TRIAL REGISTRATION

This study was prospectively registered at clinicaltrials.gov (clinicaltrials.gov NCT01202331).

Mass drug administration: the importance of synchrony

Mass drug administration, a strategy in which all individuals in a population are subject to treatment without individual diagnosis, has been recommended by the World Health Organization for controlling and eliminating several neglected tropical diseases, including trachoma and soil-transmitted helminths. In this article, we derive effective reproduction numbers and average post-treatment disease prevalences of a simple susceptible-infectious-susceptible epidemic model with constant, impulsive synchronized and non-synchronized drug administration strategies. In the non-synchronized model, the individuals in the population are treated at most once per period and their treatment times are uniformly distributed. Mathematically, the set of pulses for the non-synchronized model has the cardinality of the continuum. We show that synchronized and constant strategies are, respectively, the most and least effective treatments in disease control. Elimination through synchronized treatment is always possible when adequate drug efficacy and coverage are fulfilled and sustained. For a strategy with multiple rounds of synchronized treatment per period, the average post-treatment prevalence is irrelevant what the time differences between treatments are, as long as there are the same number of treatments per period.

Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison

The World Health Organization and its partners are aiming to eliminate trachoma as a public health problem by 2020. In this study, we compare forecasts of TF prevalence in 2011 for 7 different statistical and mechanistic models across 9 de-identified trachoma endemic districts, representing 4 unique trachoma endemic countries. We forecast TF prevalence between 1-6 years ahead in time and compare the 7 different models to the observed 2011 data using a log-likelihood score. An SIS model, including a district-specific random effect for the district-specific transmission coefficient, had the highest log-likelihood score across all 9 districts and was therefore the best performing model. While overall the deterministic transmission model was the least well performing model, although it did comparably well to the other models for 8 of 9 districts. We perform a statistically rigorous comparison of the forecasting ability of a range of mathematical and statistical models across multiple endemic districts between 1 and 6 years ahead of the last collected TF prevalence data point in 2011, assessing results against surveillance data. This study is a step towards making statements about likelihood and time to elimination with regard to the WHO GET2020 goals.

Mathematical Modelling of Trachoma Transmission, Control and Elimination

The World Health Organization has targeted the elimination of blinding trachoma by the year 2020. To this end, the Global Elimination of Blinding Trachoma (GET, 2020) alliance relies on a four-pronged approach, known as the SAFE strategy (S for trichiasis surgery; A for antibiotic treatment; F for facial cleanliness and E for environmental improvement). Well-constructed and parameterized mathematical models provide useful tools that can be used in policy making and forecasting in order to help to control trachoma and understand the feasibility of this large-scale elimination effort. As we approach this goal, the need to understand the transmission dynamics of infection within areas of different endemicities, to optimize available resources and to identify which strategies are the most cost-effective becomes more pressing. In this study, we conducted a review of the modelling literature for trachoma and identified 23 articles that included a mechanistic or statistical model of the transmission, dynamics and/or control of (ocular) Chlamydia trachomatis. Insights into the dynamics of trachoma transmission have been generated through both deterministic and stochastic models. A large body of the modelling work conducted to date has shown that, to varying degrees of effectiveness, antibiotic administration can reduce or interrupt trachoma transmission. However, very little analysis has been conducted to consider the effect of nonpharmaceutical interventions (and particularly the F and E components of the SAFE strategy) in helping to reduce transmission. Furthermore, very few of the models identified in the literature review included a structure that permitted tracking of the prevalence of active disease (in the absence of active infection) and the subsequent progression to disease sequelae (the morbidity associated with trachoma and ultimately the target of GET 2020 goals). This represents a critical gap in the current trachoma modelling literature, which makes it difficult to reliably link infection and disease. In addition, it hinders the application of modelling to assist the public health community in understanding whether trachoma programmes are on track to reach the GET goals by 2020. Another gap identified in this review was that of the 23 articles examined, only one considered the cost-effectiveness of the interventions implemented. We conclude that although good progress has been made towards the development of modelling frameworks for trachoma transmission, key components of disease sequelae representation and economic evaluation of interventions are currently missing from the available literature. We recommend that rapid advances in these areas should be urgently made to ensure that mathematical models for trachoma transmission can robustly guide elimination efforts and quantify progress towards GET 2020.

Serological Measures of Trachoma Transmission Intensity

Ocular infection with Chlamydia trachomatis can lead to trachoma, a leading infectious cause of blindness. Trachoma is targeted for elimination by 2020. Clinical grading for ocular disease is currently used for evaluating trachoma elimination programs, but serological surveillance can be a sensitive measure of disease transmission and provide a more objective testing strategy than clinical grading. We calculated the basic reproduction number from serological data in settings with high, medium, and low disease transmission based on clinical disease. The data showed a striking relationship between age seroprevalence and clinical data, demonstrating the proof-of-principle that age seroprevalence predicts transmission rates and therefore could be used as an indicator of decreased transmission of ocular trachoma.

Short-term forecasting of the prevalence of clinical trachoma: utility of including delayed recovery and tests for infection

Background

The World Health Organization aims to control blinding trachoma by 2020. Decisions on whether to start and stop mass treatments and when to declare that control has been achieved are currently based on clinical examination data generated in population-based surveys. Thresholds are based on the district-level prevalence of trachomatous inflammation–follicular (TF) in children aged 1–9 years. Forecasts of which districts may and may not meet TF control goals by the 2020 target date could affect resource allocation in the next few years.

Methods

We constructed a hidden Markov model fit to the prevalence of two clinical signs of trachoma and PCR data in 24 communities from the recent PRET-Niger trial. The prevalence of TF in children in each community at 36 months was forecast given data from earlier time points. Forecasts were scored by the likelihood of the observed results. We assessed whether use of TF with additional TI and PCR data rather than just the use of TF alone improves forecasts, and separately whether incorporating a delay in TF recovery is beneficial.

Results

Including TI and PCR data did not significantly improve forecasts of TF. Forecasts of TF prevalence at 36 months by the model with the delay in TF recovery were significantly better than forecasts by the model without the delay in TF recovery (p = 0.003). A zero-inflated truncated normal observation model was better than a truncated normal observation model, and better than a sensitivity-specificity observation model.

Conclusion

The results in this study suggest that future studies could consider using just TF data for forecasting, and should include a delay in TF recovery.

Trial registration

Clinicaltrials.gov NCT00792922

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-015-1115-8) contains supplementary material, which is available to authorized users.

Possible changes in the transmissibility of trachoma following MDA and transmission reduction: implications for the GET2020 goals

BACKGROUND

The role of mass drug administration (MDA) and the implementation of transmission reduction measures are essential to successfully control and eliminate a wide range of NTDs, including the ocular disease trachoma. Immunity to trachoma infection acts by reducing the duration of an individual's infectious period and by reducing their infectivity with each successive infection.

METHODS

In this study, we use a model of trachoma infection, which includes population immunity, to explore the impact of treatment and transmission reduction measures on trachoma prevalence. Specifically, we investigate the possibility of increasing transmissibility of trachoma arising as MDA and transmission reduction measures are scaled up in endemic settings.

RESULTS

We demonstrate this increase in transmissibility by calculating the effective reproduction number during several simulated control programmes and show that it is related to a decrease in the level of immunity in the population.

CONCLUSIONS

This effect should be studied in the field by measuring the rate of return of infection and disease in at least two separate age groups. If the decline of population immunity is operating, it should be accounted for when planning for the GET2020 goal of eliminating blinding trachoma by 2020.

Short-term Forecasting of the Prevalence of Trachoma: Expert Opinion, Statistical Regression, versus Transmission Models

Background

Trachoma programs rely on guidelines made in large part using expert opinion of what will happen with and without intervention. Large community-randomized trials offer an opportunity to actually compare forecasting methods in a masked fashion.

Methods

The Program for the Rapid Elimination of Trachoma trials estimated longitudinal prevalence of ocular chlamydial infection from 24 communities treated annually with mass azithromycin. Given antibiotic coverage and biannual assessments from baseline through 30 months, forecasts of the prevalence of infection in each of the 24 communities at 36 months were made by three methods: the sum of 15 experts’ opinion, statistical regression of the square-root-transformed prevalence, and a stochastic hidden Markov model of infection transmission (Susceptible-Infectious-Susceptible, or SIS model). All forecasters were masked to the 36-month results and to the other forecasts. Forecasts of the 24 communities were scored by the likelihood of the observed results and compared using Wilcoxon’s signed-rank statistic.

Findings

Regression and SIS hidden Markov models had significantly better likelihood than community expert opinion (p = 0.004 and p = 0.01, respectively). All forecasts scored better when perturbed to decrease Fisher’s information. Each individual expert’s forecast was poorer than the sum of experts.

Interpretation

Regression and SIS models performed significantly better than expert opinion, although all forecasts were overly confident. Further model refinements may score better, although would need to be tested and compared in new masked studies. Construction of guidelines that rely on forecasting future prevalence could consider use of mathematical and statistical models.

Forecasts of infectious diseases are rarely made in a falsifiable manner. Trachoma trials offer an opportunity to actually compare forecasting methods in a masked fashion. The World Health Organization recommends at least three annual antibiotic mass drug administrations where the prevalence of trachoma is greater than 10% in children aged 1–9 years, with coverage at least at 80%. The Program for the Rapid Elimination of Trachoma trials estimated longitudinal prevalence of ocular chlamydial infection from 24 communities treated annually with mass azithromycin. Here, we compared forecasts of the prevalence of infection in each of the 24 communities at 36 months (given antibiotic coverage and biannual assessments from baseline through 30 months, and masked to the 36-month assessments) made by experts, statistical regression, and a transmission model. The transmission model was better than regression, with both far better than experts’ opinion. Construction of guidelines that rely on forecasting future prevalence could consider use of mathematical and statistical models.

The Allee effect and elimination of neglected tropical diseases: a mathematical modelling study

Elimination and control programmes for neglected tropical diseases (NTDs) are underway around the world, yet they are generally informed by epidemiological modelling only to a rudimentary degree. Chief among the modelling-derived predictors of disease emergence or controllability is the basic reproduction number R0. The ecological systems of several of the NTDs include density-dependent processes--which alter the rate of e.g. parasite establishment or fecundity--that complicate the calculation of R0. Here we show how the forms of the density-dependent functions for a model of the NTD lymphatic filariasis affect the effective reproduction number Reff. We construct infection transmission models containing various density-dependent functions and show how they alter the shape of the Reff profile, affecting two important epidemiological outcome variables that relate to elimination and control programmes: the parasite transmission breakpoint (or extinction threshold) and the reproduction fitness, as measured by Reff. The current drive to control, eliminate or eradicate several parasitic infections would be substantially aided by the existence of ecological Allee effects. For these control programmes, the findings of this paper are encouraging, since a single positive density dependency (DD) can introduce a reasonable chance of achieving elimination; however, there are diminishing returns to additional positive DDs.

The efficacy of oral azithromycin in clearing ocular chlamydia: mathematical modeling from a community-randomized trachoma trial

Mass oral azithromycin distributions have dramatically reduced the prevalence of the ocular strains of chlamydia that cause trachoma. Assessing efficacy of the antibiotic in an individual is important in planning trachoma elimination. However, the efficacy is difficult to estimate, because post-treatment laboratory testing may be complicated by nonviable organisms or reinfection. Here, we monitored ocular chlamydial infection twice a year in pre-school children in 32 communities as part of a cluster-randomized clinical trial in Tanzania (prevalence in children was lowered from 22.0% to 4.7% after 3-year of annual treatment). We used a mathematical transmission model to estimate the prevalence of infection immediately after treatment, and found the effective field efficacy of antibiotic in an individual to be 67.6% (95% CI: 56.5–75.1%) in this setting. Sensitivity analyses suggested that these results were not dependent on specific assumptions about the duration of infection. We found no evidence of decreased efficacy during the course of the trial. We estimated an 89% chance of elimination after 10 years of annual treatment with 95% coverage.