Evidence map›Paper›PMID 42136595›Full record

ArticleFrontiers in public health2026

The impact of El Niño-Southern Oscillation on the incidence of infectious diarrhea in China: insights from a 15-year national surveillance analysis.

Keer Ou, Xing Li, Weilin Zeng, Yue Shi, Zuhua Rong, Yingtao Zhang, Shu Xiao, Zhongyi Fan, Mengjie Geng, Hongwei Tu and 1 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Keer Ou *School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China.
Xing Li *Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Weilin Zeng *Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Yue ShiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Division of Infectious Disease, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.
Zuhua RongGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Yingtao ZhangGuangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Shu XiaoGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Zhongyi FanGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Mengjie GengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Division of Infectious Disease, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.
Hongwei TuGuangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Jianpeng XiaoSchool of Public Health, Guangdong Pharmaceutical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

DiarrheaEl Nino-Southern OscillationChinaHumansIncidenceWavelet Analysisclimate factorEl Niño Southern Oscillationgeneralized additive modelinfectious diarrheawavelet analysis

Identifiers

PMID42136595
PMCPMC13168187

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.