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The association between climate change and Lyme disease incidence in Northern European countries by the Baltic and North Seas: An ecological time-series study

Alhamadn, M. A.

2025-08-27 public and global health
10.1101/2025.08.24.25334298 medRxiv
Show abstract

Climate change is a complex problem that often disproportionately affects global health, including its influence on microbial ecosystems, which can lead to disease outbreaks such as Lyme disease (LD). However, the extent of climate variability and its influence on the spread of the most common vector-borne disease in countries neighboring the Baltic Sea and North Sea have not been fully quantified. Therefore, this study quantified the magnitude of the (LD) burden due to climate change in the most at-risk European countries. In this ecological study, the correlation between disease incidence over the years and climate change was tested using a Spearman correlation test, and the change in incidence of LD during the period 2000-2024 was assessed using Generalized Linear Models (GLMs) with a negative binomial distribution to handle overdispersion. The study found a strong positive relationship when accounting for the tick life cycle using lagged climate variables of two years to higher rates of LD in countries bordering the Baltic and North Seas. Most importantly, a unit increase in precipitation (mm/year), after adjusting for covariates, is associated with a higher disease rate (IRR Range= 1.15-1.24). Two years of delayed temperature effect has a similar relationship (IRR range = 1.11-1.27). These findings suggest that disproportionate climate change in the region influences the spread and burden of the disease in the northern European temperate climate, mostly observed after two years of delays. Compared to previous research, this study focused on the regional impact of climate change on microbial life and disease spillover, which causes public health problems. There is an urgent need for a collaborative and comprehensive program that includes environmental, human, animal and vector data factors for future research to aid in disease control.

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