Biomarker dynamics: estimating infection rates from serological data

Abstract

The marginal distribution of serum antibody titres in a cross-sectional population sample can be expressed as a function of the infection rate, taking into account heterogeneity in peak levels and decay rates. This marginal model allows estimation of incidences, as well as simple tests for homogeneity across age, gender or geographic strata, using likelihood ratio tests. An example is given using Campylobacter serum antibody data. Using a hierarchical dynamic model to analyse data from a follow-up study in patients with symptomatic Campylobacter infection, we show that the serum antibody response consists of a rapid increase to peak levels followed by a slow decline with a geometric mean halftime of 1.4, 0.6 and 0.3 years for IgG, IgM and IgA, respectively. Antibody peak levels and decay rates were highly variable among subjects. Incidence estimates are consistent among different antibody classes (IgG, IgM and IgA). High seroconversion rates indicate that Campylobacter infection is a frequent event, occurring approximately once every year in any adult person, in the Netherlands, supporting the conclusion that a small fraction of infections leads to symptoms severe enough for notification.