ArticleOsong public health and research perspectives2025
Scrub typhus in the era of climate change: exploring lagged and cumulative effects of meteorological factors in the Republic of Korea, 2001-2024, a nationwide time-series study.
Article in Osong public health and research perspectives, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
objectivesScrub typhus, caused by Orientia tsutsugamushi, is a climate-sensitive vector-borne disease with high incidence in the Republic of Korea. This study examined long-term epidemiological trends and changing meteorological influences in the context of climate change.
methodsA retrospective time-series study was conducted using national surveillance data on 149,289 scrub typhus cases (2001-2024) across 4 surveillance phases. Temporal trends in age-standardized incidence rates were evaluated using Joinpoint regression. Associations between monthly meteorological variables and incidence were assessed with Spearman correlation analysis and time-series regression analysis using distributed lag non-linear models.
resultsThe national incidence increased until 2017 and has decreased since 2018, whereas the AAPC rebound to 4.32% during phase IV (2019-2024). The proportion of female cases decreased, while that of adults ≥70 years increased significantly. In phase IV, the average annual percent change increased in central and urban regions. The lag effect of meteorological factors lengthened from 4 to 6 months, with mean temperature (Tmean) and relative humidity (RH) representing the primary predictors. Phase III (2013-2018) displayed the highest cumulative relative risk (RR) for Tmean at 25.2 °C (RR, 5.86; 95% confidence interval [CI], 2.56-13.42), whereas in phase IV, only moderate RH (58%) remained significantly associated with incidence (RR, 1.68; 95% CI, 1.29-2.20).
conclusionOver the past 2 decades, the influence of meteorological factors on scrub typhus has shifted, with recent years marked by greater uncertainty under increasing climate variability and instability. For timely risk prediction and targeted prevention, adaptive surveillance systems that integrate dynamic climate indicators-capturing the intensity, frequency, and variability of extreme weather events-are needed.
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