Effects of ambient temperature on daily emergency department visits for abdominal pain: a distributed lag nonlinear time series analysis in Wuhan
Manting Lin , Shaozhou Ni , Xiaoqing Jin
Emergency and Critical Care Medicine ›› 2026, Vol. 6 ›› Issue (1) : 16 -24.
Background: Ambient temperature has been shown to have a positive association with the increasing emergency department burden. However, no studies have investigated the effects of ambient temperature on the number of emergency department visits (EDVs) for abdominal pain. Therefore, we conducted this study to evaluate the relationship between the two, with the aim of rationally allocating medical resources.
Methods: We collected daily numbers of EDVs for abdominal pain data from the Zhongnan Hospital of Wuhan University and daily meteorological data from Wuhan from January 1, 2016, to December 31, 2018. We chose a generalized additive model combined with a distributed lag nonlinear model to assess the short-term effects of ambient temperature on EDVs for abdominal pain. We conducted stratification analyses according to sex, age, and the Chinese Emergency Triage Scale.
Results: A total of 16,318 visits for abdominal pain were identified during the study period. The significant effects of extremely low temperature (−1°C) and moderately high temperature (26°C) were observed at Lag 0 day and Lag 0-1 day models. In the Lag 0 day model, the relative risks for extremely low and moderately high temperatures were 0.83 (95% confidence interval [CI]: 0.70-0.99; P = 0.038) and 1.15 (95% CI: 1.06-1.25; P = 0.001), respectively. Younger people were more likely to be affected by temperature. Cold and mildly hot weather were associated with the L2 and L3 levels, respectively. Moreover, low temperatures were negatively correlated with the risk of urolithiasis and cholecystitis.
Conclusion: Low temperatures significantly reduced abdominal pain-related EDVs, whereas high temperatures had the opposite effect.
Abdominal pain / Ambient temperature / Chinese emergency triage scale / Distributed lag nonlinear model / Emergency department visits / Time-series study
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