Abstract: OBJECTIVE To explore the independent lagged effects of short-term meteorological fluctuations on the daily visits of other infectious diarrhea, providing a quantitative basis for constructing prospective nursing response strategies driven by meteorological early warning. METHODS Data from 1 290 cases of other infectious diarrhea in the outpatient department of a three-A hospital in Jinan during the summer of 2024, along with concurrent meteorological data, were collected. Spearman correlation analysis was used to assess the association between various meteorological factors and daily visits at lag periods of 0-14 days. A generalized additive model (GAM) incorporating moving average detrending variables was constructed to remove long-term seasonal trends and investigate the nonlinear lagged relationship between short-term meteorological fluctuations and daily visits. RESULTS The strongest positive correlation between daily mean temperature and daily visits occurred at a lag of 4 days (r=0.585, P<0.001), while the strongest negative correlation with sea-level pressure was observed at a lag of 9 days (r=-0.686, P<0.001). GAM analysis revealed that, after controlling for seasonal trends, short-term increases in mean temperature at lags of 0-4 days showed a significant nonlinear positive correlation with diarrhea visit risk, peaking when temperatures were approximately 0.50°C above the seasonal trend (high-risk window: 0.50-1.00°C). In contrast, short-term fluctuations in sea-level pressure at lags of 7-11 days exhibited a nearly linear negative correlation, with significantly elevated risks when pressure fell below -1.00 hPa of the seasonal trend, while pressures above 1.00 hPa demonstrated a significant protective effect. CONCLUSIONS Short-term temperature spikes and decreases in sea-level pressure are key exposure factors driving increased visit risks for other infectious diarrhea, whereas positive fluctuations in sea-level pressure play a notable protective role. This quantitative relationship provides a scientific basis for establishing meteorological early warning systems, guiding prospective allocation of diagnosis and treatment resources, and developing dynamic intervention plans.