Baseline, Impact and Surveillance Trachoma Prevalence Surveys in Burundi, 2018-2021.

PURPOSE: Trachoma is an eye disease caused by the bacterium Chlamydia trachomatis (Ct). It can lead to permanent vision loss. Since 2007, Burundi has included trachoma elimination as part of its fight against neglected tropical diseases and blindness. This study presents the results of trachoma baseline, impact and surveillance surveys conducted in Burundi between 2018 and 2021. METHODS: Areas were grouped into evaluation units (EU) with resident populations of between 100,000 and 250,000 people. Baseline surveys were conducted in 15 EUs, impact surveys in 2 EUs and surveillance surveys in 5 EUs; in each survey, 23 clusters of about 30 households were included. Consenting residents of those households were screened for clinical signs of trachoma. Access to water, sanitation and hygiene (WASH) was recorded. RESULTS: A total of 63,800 individuals were examined. The prevalence of TF in 1-9-year-olds was above the elimination threshold of 5% in a single EU at baseline, but fell below the threshold in subsequent impact and surveillance surveys. The prevalence of TT was below the 0.2% elimination threshold in ≥15-year-olds in all EUs surveyed. A high proportion (83%) of households had access to safe drinking water, while only a minority (~8%) had access to improved latrines. CONCLUSION: Burundi has demonstrated the prevalence levels necessary for trachoma elimination status. With continued effort and the maintenance of existing management plans, trachoma elimination in Burundi is within reach.

Progress towards onchocerciasis elimination in Côte d'Ivoire: A geospatial modelling study.

BACKGROUND: Côte d'Ivoire has had 45 years of intervention for onchocerciasis by vector control (from 1975 to 1991), ivermectin mass drug administration (MDA) (from 1992 to 1994) and community directed treatment with ivermectin (CDTi) from 1995 to the present. We modeled onchocerciasis endemicity during two time periods that correspond to the scale up of vector control and ivermectin distribution, respectively. This analysis illustrates progress towards elimination during these periods, and it has identified potential hotspots areas that are at risk for ongoing transmission. METHODS AND FINDINGS: The analysis used Ministry of Health skin snip microfilaria (MF) prevalence and intensity data collected between 1975 and 2016. Socio-demographic and environmental factors were incorporated into a predictive, machine learning algorithm to create continuous maps of onchocerciasis endemicity. Overall predicted mean MF prevalence decreased from 51.8% circa 1991 to 3.9% circa 2016. The model predicted infection foci with higher prevalence in the southern region of the country. Predicted mean community MF load (CMFL) decreased from 10.1MF/snip circa 1991 to 0.1MF/snip circa 2016. Again, the model predicts foci with higher Mf densities in the southern region. For assessing model performance, the root mean squared error and R2 values were 1.14 and 0.62 respectively for a model trained with data collected prior to 1991, and 1.28 and 0.57 for the model trained with infection survey data collected later, after the introduction of ivermectin. Finally, our models show that proximity to permanent inland bodies of water and altitude were the most informative variables that correlated with onchocerciasis endemicity. CONCLUSION/SIGNIFICANCE: This study further documents the significant reduction of onchocerciasis infection following widespread use of ivermectin for onchocerciasis control in Côte d'Ivoire. Maps produced predict areas at risk for ongoing infection and transmission. Onchocerciasis might be eliminated in Côte d'Ivoire in the future with a combination of sustained CDTi with high coverage, active surveillance, and close monitoring for persistent infection in previously hyper-endemic areas.

The global burden of trichiasis in 2016.

BACKGROUND: Trichiasis is present when one or more eyelashes touches the eye. Uncorrected, it can cause blindness. Accurate estimates of numbers affected, and their geographical distribution, help guide resource allocation. METHODS: We obtained district-level trichiasis prevalence estimates in adults for 44 endemic and previously-endemic countries. We used (1) the most recent data for a district, if more than one estimate was available; (2) age- and sex-standardized corrections of historic estimates, where raw data were available; (3) historic estimates adjusted using a mean adjustment factor for districts where raw data were unavailable; and (4) expert assessment of available data for districts for which no prevalence estimates were available. FINDINGS: Internally age- and sex-standardized data represented 1,355 districts and contributed 662 thousand cases (95% confidence interval [CI] 324 thousand-1.1 million) to the global total. Age- and sex-standardized district-level prevalence estimates differed from raw estimates by a mean factor of 0.45 (range 0.03-2.28). Previously non- stratified estimates for 398 districts, adjusted by ×0.45, contributed a further 411 thousand cases (95% CI 283-557 thousand). Eight countries retained previous estimates, contributing 848 thousand cases (95% CI 225 thousand-1.7 million). New expert assessments in 14 countries contributed 862 thousand cases (95% CI 228 thousand-1.7 million). The global trichiasis burden in 2016 was 2.8 million cases (95% CI 1.1-5.2 million). INTERPRETATION: The 2016 estimate is lower than previous estimates, probably due to more and better data; scale-up of trichiasis management services; and reductions in incidence due to lower active trachoma prevalence.

Understanding the spatial distribution of trichiasis and its association with trachomatous inflammation-follicular.

BACKGROUND: Whilst previous work has identified clustering of the active trachoma sign "trachomatous inflammation-follicular" (TF), there is limited understanding of the spatial structure of trachomatous trichiasis (TT), the rarer, end-stage, blinding form of disease. Here we use community-level TF prevalence, information on access to water and sanitation, and large-scale environmental and socio-economic indicators to model the spatial variation in community-level TT prevalence in Benin, Cote d'Ivoire, DRC, Guinea, Ethiopia, Malawi, Mozambique, Nigeria, Sudan and Uganda. METHODS: We fit binomial mixed models, with community-level random effects, separately for each country. In countries where spatial correlation was detected through a semi-variogram diagnostic check we then fitted a geostatistical model to the TT prevalence data including TF prevalence as an explanatory variable. RESULTS: The estimated regression relationship between community-level TF and TT was significant in eight countries. We estimate that a 10% increase in community-level TF prevalence leads to an increase in the odds for TT ranging from 20 to 86% when accounting for additional covariates. CONCLUSION: We find evidence of an association between TF and TT in some parts of Africa. However, our results also suggest the presence of additional, country-specific, spatial risk factors which modulate the variation in TT risk.