ArticleScientific reports2026
Short-term effects of meteorological factors on hand, foot, and mouth disease in Zhengzhou, China.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Hand, foot, and mouth disease (HFMD) demonstrates distinct seasonal patterns in high-incidence regions, highlighting the growing interest in the relationship between meteorological factors and disease transmission. Nevertheless, findings across different geographic areas remain highly heterogeneous. Data on HFMD cases in Zhengzhou City from 2009 to 2023 were obtained from the Zhengzhou Center for Disease Control and Prevention, while meteorological data were sourced from the National Meteorological Data Sharing Service System. Distributed lag nonlinear models (DLNM) was employed to quantify the effects of meteorological variables and extreme weather events on HFMD incidence, as well as to evaluate the impact of various lag periods. DLNM analysis revealed nonlinear exposure–response relationships between HFMD risk with daily mean temperature, relative humidity, and atmospheric pressure. Extreme weather events further elevated the risk. Daily mean temperature demonstrated the peaking relative risk (RR) at 27 °C (RR = 1.17, 95% CI: 1.007–2.349), while relative humidity at 83% corresponded to the highest RR (RR = 1.07, 95% CI: 1.021–1.121). Atmospheric pressure exhibited maximal risk at 1044 hPa (RR = 1.53, 95% CI: 1.014–2.293). These conditions correspond to the higher end of the local climatic distribution, suggesting that HFMD risk increases under relatively warm, humid, and high-pressure conditions. Meteorological factors, including daily average temperature, relative humidity, atmospheric pressure, and extreme weather events, are associated with altered HFMD incidence in Zhengzhou through nonlinear and lagged relationships. These findings provide scientific evidence for early warning systems and targeted public health interventions based on weather forecasts.
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