Using a Nonparametric Multilevel Latent Markov Model to Evaluate Diagnostics for Trachoma

David C W Mabey,Artemis Koukounari,Christl A Donnelly,Robin L Bailey,Matthew J Burton,Manoj Gambhir,Nicholas C Grassly,María-Gloria Basáñez,Irini Moustaki,Isobel M Blake,Anthony W Solomon

doi:10.1093/aje/kws345

Abstract

In disease control or elimination programs, diagnostics are essential for assessing the impact of interventions, refining treatment strategies, and minimizing the waste of scarce resources. Although high-performance tests are desirable, increased accuracy is frequently accompanied by a requirement for more elaborate infrastructure, which is often not feasible in the developing world. These challenges are pertinent to mapping, impact monitoring, and surveillance in trachoma elimination programs. To help inform rational design of diagnostics for trachoma elimination, we outline a nonparametric multilevel latent Markov modeling approach and apply it to 2 longitudinal cohort studies of trachoma-endemic communities in Tanzania (2000–2002) and The Gambia (2001–2002) to provide simultaneous inferences about the true population prevalence of Chlamydia trachomatis infection and disease and the sensitivity, specificity, and predictive values of 3 diagnostic tests for C. trachomatis infection. Estimates were obtained by using data collected before and after mass azithromycin administration. Such estimates are particularly important for trachoma because of the absence of a true “gold standard” diagnostic test for C. trachomatis. Estimated transition probabilities provide useful insights into key epidemiologic questions about the persistence of disease and the clearance of infection as well as the required frequency of surveillance in the postelimination setting.

Highlights

We developed latent Markov models (LMMs), which we applied to 2 longitudinal data sets from Tanzanian and Gambian communities with low baseline trachoma prevalence before and after mass administration of azithromycin
We applied nonparametric multilevel LMMs to 2 longitudinal data sets from Tanzanian and Gambian communities with low baseline trachoma prevalence before and after a round of mass azithromycin administration. This latent variable modeling approach yielded an average assessment of the diagnostic accuracy of polymerase chain reaction (PCR), trachomatous inflammation (TF), and trachomatous inflammation (TI) for the detection of C. trachomatis infection in the absence of a gold standard
The sensitivity and positive predictive value of clinical examination for infection were estimated to be very low in The Gambia, whereas the sensitivity of TI and positive predictive value of clinical examination (TF and TI) were very low in Tanzania, which is problematic; if treatment decisions are based solely on the simplified grading system, it could potentially lead to the decision to continue the annual mass administration of antichlamydial antibiotics for years after C. trachomatis has been eliminated from recipient communities [39]

Summary

Introduction

Polymerase chain reaction (PCR)–based assays are considered to be very sensitive and specific for the diagnosis of C. trachomatis infection, they are currently unavailable in trachoma-endemic settings and too expensive to be used routinely as impact evaluation tools in large-scale treatment programs. The diagnostic performance of clinical examination is hindered by the frequent presence of active disease in the absence of infection and of infection in the absence of active disease [4,5,6] This is partly because clinical signs of trachoma may persist for many weeks after infection has been cleared [6,7,8] and may result when infections other than Chlamydia cause trachoma-like inflammatory disease [9].

Methods

Results

Conclusion