The impact of El Niño-Southern Oscillation on the incidence of infectious diarrhea in China

Insights from a 15-year national surveillance analysis

Bibliographic Data

ID22068697
AuthorsKeer Ou (Guangdong Pharmaceutical University), Xing Li (0000-0001-5578-3901, Guangdong Provincial Center for Disease Control and Prevention), Weilin Zeng (Guangdong Provincial Center for Disease Control and Prevention), Yue Shi (0000-0002-3352-9045, Chinese Center For Disease Control and Prevention), Zuhua Rong (Guangdong Provincial Center for Disease Control and Prevention), Ye Zhang (0009-0002-2442-4804, Guangdong Provincial Center for Disease Control and Prevention), Yingtao Zhang (0000-0002-8587-9645, Guangdong Provincial Center for Disease Control and Prevention), Xiao Shu (0000-0002-5388-5282, Guangdong Provincial Center for Disease Control and Prevention), Shu Xiao (Guangdong Provincial Center for Disease Control and Prevention), Zhongyi Fan (Guangdong Provincial Center for Disease Control and Prevention), Mengjie Geng (0000-0003-2578-940X, Chinese Center For Disease Control and Prevention), Hongwei Tu (0000-0001-6800-6052, Guangdong Provincial Center for Disease Control and Prevention), Jianpeng Xiao (0000-0001-9665-6036, Guangdong Pharmaceutical University)
Year2026
Volume14
Pages1791469-1791469
Publication date2026-04-29
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2026.1791469
PMID42136595
OpenAlexW7159573702
LanguageEN
References cited47

Background: Infectious diarrhea remains a significant public health challenge, with climatic factors potentially playing a crucial role in its epidemiological spread. However, the precise mechanisms through which the El Niño-Southern Oscillation (ENSO) influences diarrheal morbidity are still not fully understood. Methods: We collected monthly other infectious diarrhea (OID) incidence and climatic data across the 31 provincial administrative divisions of mainland China (2005-2019). Wavelet analysis was employed to examine the periodicity of OID and the phase relationships between ENSO, climate factors and OID. Generalized additive models (GAM) and Peter and Clark Momentary Conditional Independence (PCMCI) algorithm were used to quantify exposure-response relationship and establish causal pathways in China. Results: From 2005 to 2019, a total of 13,620,167 OID cases were reported in 31 provincial regions of China, with the highest incidence of OID concentrated in the southern and eastern regions of China. Wavelet analysis identified a significant periodicity between ENSO cycles and diarrhea incidence patterns, demonstrating that La Niña events (characterized by low ENSO index) were associated with subsequent increases in incidence with a 6-month lag. The exposure-response relationship showed an inverted J-shaped curve in North and East China, while a nearly linear relationship was observed in Northeast, Central, Southwest and Northwest China. PCMCI analysis elucidated that precipitation is an indirect link between ENSO and OID. Conclusion: Our study suggests that a low ENSO index (La Niña) may drive the incidence of OID in China. The findings provide a scientific basis for predicting and warning of OID based on ENSO

Diarrhea · El Niño Southern Oscillation · Public health · Spectral analysis · Climate Change and Health Impacts · Climate variability and models · Zoonotic diseases and public health · Epidemiology

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