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

Sanitation and water supply coverage thresholds associated with active trachoma: Modeling cross-sectional data from 13 countries.

BACKGROUND: Facial cleanliness and sanitation are postulated to reduce trachoma transmission, but there are no previous data on community-level herd protection thresholds. We characterize associations between active trachoma, access to improved sanitation facilities, and access to improved water sources for the purpose of face washing, with the aim of estimating community-level or herd protection thresholds. METHODS AND FINDINGS: We used cluster-sampled Global Trachoma Mapping Project data on 884,850 children aged 1-9 years from 354,990 households in 13 countries. We employed multivariable mixed-effects modified Poisson regression models to assess the relationships between water and sanitation coverage and trachomatous inflammation-follicular (TF). We observed lower TF prevalence among those with household-level access to improved sanitation (prevalence ratio, PR = 0.87; 95%CI: 0.83-0.91), and household-level access to an improved washing water source in the residence/yard (PR = 0.81; 95%CI: 0.75-0.88). Controlling for household-level water and latrine access, we found evidence of community-level protection against TF for children living in communities with high sanitation coverage (PR80-90% = 0.87; 95%CI: 0.73-1.02; PR90-100% = 0.76; 95%CI: 0.67-0.85). Community sanitation coverage levels greater than 80% were associated with herd protection against TF (PR = 0.77; 95%CI: 0.62-0.97)-that is, lower TF in individuals whose households lacked individual sanitation but who lived in communities with high sanitation coverage. For community-level water coverage, there was no apparent threshold, although we observed lower TF among several of the higher deciles of community-level water coverage. CONCLUSIONS: Our study provides insights into the community water and sanitation coverage levels that might be required to best control trachoma. Our results suggest access to adequate water and sanitation can be important components in working towards the 2020 target of eliminating trachoma as a public health problem

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

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

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

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

Proportion of participants with different phenotypes considered seropositive by each threshold.

<p>Proportion of participants with different phenotypes considered seropositive by each threshold.</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