Characterizing trachoma elimination using serology

Abstract

Trachoma is targeted for global elimination as a public health problem by 2030. Measurement of IgG antibodies in children is being considered for surveillance and programmatic decision-making. There are currently no guidelines for applications of serology, which represents a generalizable problem in seroepidemiology and disease elimination. We collated Chlamydia trachomatis Pgp3 and CT694 IgG measurements (63,911 children ages 1-9 years) from 48 serosurveys, including surveys across Africa, Latin America, and the Pacific Islands to estimate population-level seroconversion rates (SCR) along a gradient of trachoma endemicity. We propose a novel, generalizable approach to estimate the probability that population C. trachomatis transmission is below levels requiring ongoing programmatic action, or conversely is above levels that indicate ongoing interventions are needed. We provide possible thresholds for SCR at a specified level of certainty and illustrate how the approach could be used to inform trachoma program decision-making using serology.

Fig 1.. Age-specific Pgp3 IgG seroprevalence among 1–9-year-olds.

Evaluation unit (EU)-level seroprevalence to Chlamydia trachomatis Pgp3 antigen among children aged 1–9 years (N=48 evaluation units, and 63,911 children). Lines represent mean seroprevalence by age estimated using semiparametric cubic splines and EUs are grouped by categories based on programmatic responses (Methods). “Action needed” EUs include populations with clear evidence of ongoing transmission that require public health control measures, while “Action not needed” EUs include populations with demonstrated trachoma control. Unclassified EUs were used as a held-out sample in the analyses. The shaded region in each panel identifies the age range used in the main analyses: 1–5 years (41,168 children). Table 1 includes EU-specific sample sizes.

Fig 2.. Seroconversion rate (SCR) per 100 person-years in 1–5-year-olds.

A. Density distributions of the SCR for 34 evaluation units (N=41,168 children). For each evaluation unit, the black vertical line shows the median estimate, and the density distributions depict the uncertainty about the median. EUs are colored by programmatic response category (Methods) and ordered by increasing median SCR value. The unclassified evaluation units are shown in Fig S1. B. Pooled density distributions of the SCR for each category.

Fig 3.. Posterior probability of the need for population-level trachoma interventions using seroconversion rate.

Posterior probability of programmatic Àction not needed` versus Àction needed` categories along a range of seroconversion rates (SCRs) among 1–5-year-olds calculated using a two-component Bayesian mixture model (Methods). A. Posterior functions assume moderately informative prior probabilities of 80% Àction not needed` and 20% Àction needed`. In principle, the posterior probability functions allow for the selection of thresholds to inform decisions based on serological surveys with a desired level of certainty. For example, at a ≥90% level of certainty, SCR of ≤2.2 per 100 person-years corresponds to a posterior probability of Àction not needed` and a SCR of ≥4.5 corresponds to a posterior probability of Àction needed`. SCR values >2.2 and <4.5 per 100 person-years may require additional information to inform programmatic action. B. Posterior functions assume an uninformative prior of 50% Àction not needed` and 50% Àction needed`. Sensitivity analyses in Fig S3 demonstrate that posterior probabilities are insensitive to the prior assumptions.

Fig 4.. Sensitivity analysis of exclusion of evaluation unit- and country-level data.

A jackknife n-1 resampling approach was used to iteratively alter group composition in a Bayesian Mixture model, with estimates based on the seroconversion rate (SCR) among 1–5-year-olds (Methods). A. Posterior probability of No Action Needed for trachoma control removing each of 34 evaluation units in turn. B. Posterior probability of No Action Needed for trachoma control as in panel A, but removing entire countries. All estimates assumed an 80% prior probability of no action needed. The gray lines and dots in each panel show results of the resampling approach, and the brown line and dots show results from the analysis using the original full dataset (N=34 EUs and N=10 countries). The open circle with a `x` symbol in indicates the mean SCR per person-years over the n jackknife subsamples. The two most influential held-out units are labeled in each sensitivity analysis. Overall, there was minimal effect of removing data at EU- or country-level in the higher posterior probabilities – our primary focus. More so, the effect was less pronounced in lower posterior probabilities and for the most part, there was an overlap of posterior probabilities and corresponding SCR values of the reduced datasets with that of the original full sample.

Fig 5.. Posterior probability estimates for held-out evaluation units.

A. Probability of need for trachoma program intervention in held-out evaluation units (EUs). Held-out EUs included baseline surveys in new populations that did not have PCR data (Sudan, Peru), opportunistic surveys not focused on trachoma (Malaysia), settings with unusual epidemiology based on trachoma biomarkers (Papua New Guinea, Vanuatu), and those that failed to achieve a consensus classification into Àction not needed` and Àction needed` categories (five from Ethiopia and Malawi). The posterior probability was calculated using seroconversion rate (SCR) estimates among 1–5-year-olds in a Bayesian mixture model that assumed prior probabilities of 80% for Àction not needed` and 20% for Àction needed`. EUs are ordered by increasing median SCR value shown in panel B. B. EU-specific SCR density distributions, with an example threshold shown at 2.2 per 100 person-years. C. An illustrative threshold of 2.2 corresponding to the 90% posterior probability (‘+’ in Fig 3) was used to calculate the empirical probability of Àction not needed` as the proportion of the SCR density distribution ≤2.2 per 100 person-years. Table 1 includes additional details for the unclassified EU populations that were used in the held-out analysis.