Zhang Ying, Bi Peng, Hiller Janet E
Discipline of Public Health, University of Adelaide, Adelaide, South Australia.
Public Health Rep. 2008 Jan-Feb;123(1):61-6. doi: 10.1177/003335490812300109.
This article aims to quantify the relationship between weather variations and bacillary dysentery in Jinan, a city in northern China with a temperate climate, to reach a better understanding of the effect of weather variations on enteric infections.
The weather variables and number of cases of bacillary dysentery during the period 1987-2000 has been studied on a monthly basis. The Spearman correlation between each weather variable and dysentery cases was conducted. Seasonal autoregressive integrated moving average (SARIMA) models were used to perform the regression analyses.
Maximum temperature (one-month lag), minimum temperature (one-month lag), rainfall (one-month lag), relative humidity (without lag), and air pressure (one-month lag) were all significantly correlated with the number of dysentery cases in Jinan. After controlling for the seasonality, lag time, and long-term trend, the SARIMA model suggested that a 1 degree C rise in maximum temperature might relate to more than 10% (95% confidence interval 10.19, 12.69) increase in the cases of bacillary dysentery in this city.
Weather variations have already affected the transmission of bacillary dysentery in China. Temperatures could be used as a predictor of the number of dysentery cases in a temperate city in northern China. Public health interventions should be undertaken at this stage to adapt and mitigate the possible consequences of climate change in the future.
本文旨在量化中国北方温带气候城市济南天气变化与细菌性痢疾之间的关系，以便更好地了解天气变化对肠道感染的影响。
对1987 - 2000年期间的天气变量和细菌性痢疾病例数进行了月度研究。对每个天气变量与痢疾病例之间进行了Spearman相关性分析。使用季节性自回归积分滑动平均（SARIMA）模型进行回归分析。
最高温度（滞后1个月）、最低温度（滞后1个月）、降雨量（滞后1个月）、相对湿度（无滞后）和气压（滞后1个月）均与济南的痢疾病例数显著相关。在控制了季节性、滞后时间和长期趋势后，SARIMA模型表明最高温度每升高1摄氏度，该市细菌性痢疾病例可能增加超过10%（95%置信区间10.19，12.69）。
天气变化已经影响了中国细菌性痢疾的传播。温度可作为中国北方温带城市痢疾病例数的预测指标。现阶段应采取公共卫生干预措施，以适应和减轻未来气候变化可能带来的后果。
