Estimates for Lyme borreliosis infections based on models using sentinel canine and human seroprevalence data

Published Date
Journal
Infectious Disease Modelling
Citation
5:871-888.
DOI
10.1016/j.idm.2020.10.004.
Authors
Cook MJ
Puri BK
Abstract

Two models were developed to estimate Lyme borreliosis (LB) cases. One was based on the seroprevalence of Borrelia infections in human samples. This model used corrections for false negative and false positive results from published test sensitivity and specificity measures. A second model based on Borrelia infections in sentinel dogs was used to quantify the prevalence of Lyme disease Borrelia infections in humans; the reference baseline for this model was human and canine infections in Germany. A comparison of the two models is shown and differences discussed. The relationships between incidence, prevalence and total infection burden for LB were derived from published data and these were used in both models to calculate annual incidence, prevalence and total LB infections. The modelling was conservative and based on medical insurance records coded for erythema migrans. Linear model growth rates were used in place of the commonly adopted exponential growth. The mean of the two models was used to create estimates for various countries and continents. Examples from the analyses for LB estimated for 2018 include: incidence - USA 473,000/year, Germany 471,000/year, France 434,000/year and UK 132,000/year; prevalence - USA 2.4 million, Germany 2.4 million, France 2.2 million and UK 667,000; total infections - USA 10.1 million, Germany 10.0 million, France 9.3 million and UK 2.8 million. Estimates for the world for 2018 are: incidence 12.3 million/year; prevalence 62.1 million; and total infection burden 262.0 million. These figures are far higher than officially published data and reflect not only the underestimation of diagnosed cases, which is acknowledged by health agencies, but also undiagnosed and misdiagnosed cases.

Keywords: Borreliosis; Companion animals; Linear regression models; Lyme disease; Sentinel animals; Seropositive.