Defining Seropositivity Thresholds for Use in Trachoma Elimination Studies.

BACKGROUND: Efforts are underway to eliminate trachoma as a public health problem by 2020. Programmatic guidelines are based on clinical signs that correlate poorly with Chlamydia trachomatis (Ct) infection in post-treatment and low-endemicity settings. Age-specific seroprevalence of anti Ct Pgp3 antibodies has been proposed as an alternative indicator of the need for intervention. To standardise the use of these tools, it is necessary to develop an analytical approach that performs reproducibly both within and between studies. METHODOLOGY: Dried blood spots were collected in 2014 from children aged 1-9 years in Laos (n = 952) and Uganda (n = 2700) and from people aged 1-90 years in The Gambia (n = 1868). Anti-Pgp3 antibodies were detected by ELISA. A number of visual and statistical analytical approaches for defining serological status were compared. PRINCIPAL FINDINGS: Seroprevalence was estimated at 11.3% (Laos), 13.4% (Uganda) and 29.3% (The Gambia) by visual inspection of the inflection point. The expectation-maximisation algorithm estimated seroprevalence at 10.4% (Laos), 24.3% (Uganda) and 29.3% (The Gambia). Finite mixture model estimates were 15.6% (Laos), 17.1% (Uganda) and 26.2% (The Gambia). Receiver operating characteristic (ROC) curve analysis using a threshold calibrated against external reference specimens estimated the seroprevalence at 6.7% (Laos), 6.8% (Uganda) and 20.9% (The Gambia) when the threshold was set to optimise Youden's J index. The ROC curve analysis was found to estimate seroprevalence at lower levels than estimates based on thresholds established using internal reference data. Thresholds defined using internal reference threshold methods did not vary substantially between population samples. CONCLUSIONS: Internally calibrated approaches to threshold specification are reproducible and consistent and thus have advantages over methods that require external calibrators. We propose that future serological analyses in trachoma use a finite mixture model or expectation-maximisation algorithm as a means of setting the threshold for ELISA data. This will facilitate standardisation and harmonisation between studies and eliminate the need to establish and maintain a global calibration standard

Baseline Prevalence of Trachoma in Refugee Settlements in Uganda: Results of 11 Population-based Surveys.

PURPOSE: There are several settlements in the Northern and Western Regions of Uganda serving refugees from South Sudan and Democratic Republic of Congo (DRC), respectively. Trachoma prevalence surveys were conducted in a number of those settlements with the aim of determining whether interventions for trachoma are required. METHODS: An evaluation unit (EU) was defined as all refugee settlements in one district. Cross-sectional population-based trachoma prevalence survey methodologies designed to adhere to World Health Organization recommendations were deployed in 11 EUs to assess prevalence of trachomatous inflammation-follicular (TF) in 1-9-year-olds and trachomatous trichiasis (TT) unknown to the health system in ≥15-year-olds. Household-level water, sanitation and hygiene coverage was also assessed in study populations. RESULTS: A total of 40,892 people were examined across 11 EUs between 2018 and 2020. The prevalence of TF in 1-9-year-olds was <5% in all EUs surveyed. The prevalence of trachomatous trichiasis (TT) unknown to the health system in ≥15-year-olds was <0.2% in 5 out of 11 EUs surveyed and ≥0.2% in the remaining 6 EUs. A high proportion of households had improved water sources, but a low proportion had improved latrines or quickly (within a 30-minute return journey) accessible water sources. CONCLUSIONS: Implementation of the antibiotic, facial cleanliness and environmental improvement components of the SAFE strategy is not needed for the purposes of trachoma's elimination as a public health problem in these refugee settlements; however, intervention with TT surgery is needed in six EUs. Since instability continues to drive displacement of people from South Sudan and DRC into Uganda, there is likely to be a high rate of new arrivals to the settlements over the coming years. These populations may therefore have trachoma surveillance needs that are distinct from the surrounding non-refugee communities

Completing Baseline Mapping of Trachoma in Uganda: Results of 14 Population-Based Prevalence Surveys Conducted in 2014 and 2018.

PURPOSE: We aimed to estimate the prevalence of trachomatous inflammation-follicular (TF) in children aged 1-9 years, trichiasis in adults aged ≥15 years, and water and sanitation (WASH) indicators in 12 suspected-endemic districts in Uganda. METHODS: Surveys were undertaken in 14 evaluation units (EUs) covering 12 districts. Districts were selected based on a desk review in 2014 (four districts) and trachoma rapid assessments in 2018 (eight districts). We calculated that 1,019 children aged 1-9 years were needed in each EU to estimate TF prevalence with acceptable precision and used three-stage cluster sampling to select 30 households in each of 28 (2014 surveys) or 24 (2018 surveys) villages. Participants living in selected households aged ≥1 year were examined for trachoma; thus enabling estimation of prevalences of TF in 1-9 year-olds and trichiasis in ≥15 year-olds. Household-level WASH access data were also collected. RESULTS: A total of 11,796 households were surveyed; 22,465 children aged 1-9 years and 24,652 people aged ≥15 years were examined. EU-level prevalence of TF ranged from 0.3% (95% confidence interval [CI] 0.1-0.7) to 3.9% (95% CI 2.1-5.8). EU-level trichiasis prevalence ranged from 0.01% (95% CI 0-0.11) to 0.81% (95% CI 0.35-1.50). Overall proportions of households with improved drinking water source, water source in yard or within 1km, and improved sanitation facilities were 88.1%, 23.0% and 23.9%, respectively. CONCLUSION: TF was not a public health problem in any of the 14 EUs surveyed: antibiotic mass drug administration is not required in these districts. However, in four EUs, trichiasis prevalence was ≥ 0.2%, so public health-level trichiasis surgery interventions are warranted. These findings will facilitate planning for elimination of trachoma in Uganda

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

Threshold values for Uganda (1–9 year olds) data.

<p><b>Panel A</b> shows the threshold as determined by visual inflection point analysis by 12 volunteer individuals, as detailed in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005230#pntd.0005230.g002" target="_blank">Fig 2</a>. <b>Panel B</b> shows the thresholds set by the finite mixture model and expectation-maximisation algorithm, as described in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005230#pntd.0005230.g002" target="_blank">Fig 2</a>. <b>Panel C</b> compares the threshold specifications by four different methods. Scatterplots show the normalised and sorted OD₄₅₀ values with horizontal lines marking the thresholds specified by VIP (OD₄₅₀ = 0.641), EM (OD₄₅₀ = 0.450), FMM (OD₄₅₀ = 0.554), ROC curve maximising Youden’s J-index (OD₄₅₀ = 0.870), ROC curve with sensitivity >80% (OD₄₅₀ = 0.968) and ROC curve with specificity>98% (OD₄₅₀ = 1.951).</p

Threshold values for Laos (1–9 year olds) data.

<p><b>Panel A</b> shows the threshold as determined by visual inflection point analysis by 12 volunteer individuals. Volunteers had access only to the data presented in the leftmost panels, which shows sorted OD₄₅₀ values. The second panel in A shows the density of data points for the sample while the third panel in A shows a box and whisker plots with the range of threshold values that were selected by the 12 volunteers. The box shows the inter-quartile range for the values, with the thick horizontal line marking the median value. Whiskers show the upper quartile plus 1.5x the range between the 1^st and 3^rd quartiles. Outliers are shown by an open circle. <b>Panel B</b> shows the thresholds set by the finite mixture model and expectation-maximisation algorithm. Density plots of normalised OD values and thresholds, showing the FMM estimated distribution functions of ‘seronegative’ specimens in red and ‘seropositive’ specimens in green. Vertical lines show the threshold values determined by the finite mixture model (right-most line) and the expectation-maximisation algorithm (left-most lines). <b>Panel C</b> compares the threshold specifications by four different methods. Scatterplots show the normalised and sorted OD₄₅₀ values with horizontal lines marking the thresholds specified by VIP (OD₄₅₀ = 0.619), EM (OD₄₅₀ = 0.650), FMM (OD₄₅₀ = 0.696), ROC curve maximising Youden’s J-index (OD₄₅₀ = 0.870), ROC curve with sensitivity >80% (OD₄₅₀ = 0.968) and ROC curve with specificity>98% (OD₄₅₀ = 1.951).</p

Receiver Operating Characteristic (ROC) curve showing the relationship between sensitivity, specificity and threshold values.

<p>Three different thresholds were specified to meet the requirements of: (A) an assay (threshold = 0.870 OD_450, specificity = 93.9%, sensitivity = 91.4%, PPV = 89.8%, NPV = 92.4%) with balanced sensitivity and specificity (maximal Youden’s J value); (B) an assay (threshold = 0.965 OD_450, specificity 94.8%, sensitivity = 89.4%) with at least 80% sensitivity and (C) an assay (threshold = 1.951 OD₄₅₀, specificity = 98.3%, sensitivity = 43.9%, PPV = 66.7%, NPV = 95.0%) with at least 98% specificity.</p

Typical results from an ELISA plate.

<p>Specimens are sorted by increasing OD values and are each represented by a separate diamond. The mean values of the controls tested in triplicate are represented by coloured horizontal lines.</p

Seroprevalence by Country, as estimated using alternate threshold specification methods.

<p>Seroprevalence by Country, as estimated using alternate threshold specification methods.</p