Lyme disease: modeling and analyzing long-term costs related to the infection based on Estonian Biobank data

Abstract

Lyme disease is a prevalent vector-borne illness with significant public health implications due to its potential for multisystem effects and persistent symptoms. Understanding the associated economic burden is crucial for healthcare planning. This thesis investigates the temporal dynamics of healthcare costs surrounding a Lyme disease diagnosis, aiming to quantify whether cost increases are primarily acute or persist over a longer period, which contributes to understanding the extended healthcare services utilization potentially linked to the condition. Using longitudinal health data from the Estonian Biobank, this study uses a relative time scale indexed to the year of first diagnosis. Linear Mixed-Effects (LME) models serve as the primary analytical framework to handle correlated repeated measures and model cost trajectories. The analysis compares diagnosed individuals to a reference group over a defined time window surrounding diagnosis. The thesis includes a background on the disease and methods, details the analysis, and presents results within the Estonian context.