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The tick-borne multisystemic infection caused by Borrelia burgdorferi sensu lato, Lyme borreliosis, or Lyme disease, occurring in temperate regions of the northern hemisphere, continues to spread geographically with the expanding tick population. Despite the rising perceived risk of infection in the population, the clinical diagnosis of Borrelia infection is not always obvious and the most important laboratory test, antibody detection, has limited accuracy in diagnosing active disease. According to international guidelines, the primary serology test, which has a high sensitivity-low specificity, should, be verified using a high specificity confirmation test to improve the specificity. However, this enhancement in specificity comes at the cost of lower sensitivity. This two-step procedure is often omitted in everyday clinical practice. An optimal primary test would be one where no secondary tests for confirmation would be necessary. In the present study, the performance of a novel assay for quantitating IgG1-subclass antibodies to Borrelia C6-peptide was compared to a commercial reference assay of total IgG and IgM antibodies to Borrelia C6-peptide in the setting of a high endemic area for borreliosis. A derivation study on a retrospective clinical material was performed to compare the performance parameters and assess the discriminatory properties of the assays, followed by a prospective validation study. The IgG1-antibody assay achieved comparable summary performance parameters to those of the reference assay. The sensitivity was almost 100% while the specificity was about 50%. In a high-endemic setting, characterized by high background seropositivity of about 50% and disease prevalence of approximately 10%, antibody tests are unable to rule-in active Borrelia infection. The rule-out assessment of the methods revealed that of 1000 patients, 7 – 54 with negative results based on the reference method could have an active Borrelia infection. Such uncertainty was not found for the index test and may help improve the risk classification of patients.