Demographic patterns in Lyme borreliosis seasonality over 25 years

Abstract

Abstract Lyme borreliosis, the most common vector‐borne disease in Europe and North America, is attracting growing concern due to its expanding geographic range. The growth in incidence and geographic spread is largely attributed to climate and land‐use changes that support the tick vector and thereby increase disease risk. Despite a wide range of symptoms displayed by Lyme borreliosis patients, the demographic patterns in clinical manifestations and seasonal case timing have not been thoroughly investigated and may result from differences in exposure, immunity and pathogenesis. We analysed 25 years of surveillance data from Norway, supplemented by population demography data, using a Bayesian modelling framework. The analyses aimed to detect differences in case seasonality and clinical manifestations of Lyme borreliosis across age and sex differentiated patient groups. The results showed a bimodal pattern of incidence over age, where children (0–9 years) had the highest incidence, young adults (20–29 years) had low incidence and older adults had a second incidence peak in the ages 70–79 years. Youth (0–19 years) presented with a higher proportion of neuroborreliosis cases and a lower proportion of arthritic manifestations compared to adults (20+ years). Adult males had a higher overall incidence than adult females and a higher proportion of arthritis cases. The seasonal timing of Lyme borreliosis consistently occurred around 4.4 weeks earlier in youth compared to adults, regardless of clinical manifestation. All demographic groups exhibited a shift towards an earlier seasonal timing over the 25‐year study period, which appeared unrelated to changes in population demographics. However, the disproportionate incidence of Lyme borreliosis in seniors requires increased public awareness and knowledge about this high‐risk group as the population continues to age concurrently with disease emergence. Our findings highlight the importance of considering patient demographics when analysing the emergence and seasonal patterns of vector‐borne diseases using long‐term surveillance data.