Objective: To know the incidence of heat stroke and explore it's prediction model in Pudong New Area of Shanghai. Methods: An epidemiological investigation was conducted on heat stroke cases in Pudong New Area of Shanghai from 2013 to 2017. Daily temperature data during this period were collected to explore it's influence. Results: 246 heat stroke cases were reported and investigated, 70.3% (173/246) of them were male. 170 cases are severe heat stroke, accounting for 69.1%. 28 patients died, accounting for 11.4% of all cases of heat stroke, and 16.5% (28/170) of severe heat stroke cases. Thermoplegia (56.5%, 96/170) was the most popular type among severe heat stroke cases. Heat prostration, heat cramps and mixed type account for 17.1% (29/170) , 12.4% (21/170) and 14.0% (24/170) respectively. Scatter plot and linear regression demonstrated that there was a significant linear relation between number of high temperature days and number of heat stroke cases (P<0.01) . And the prediction model is: Predictive number of annual heat stroke cases=β×Number of annual high temperature days+Intercept. Leave-one-out cross validation result shows that the predictive number of annual heat stroke cases from 2013 to 2017 were 85.7%, 90.9%, 83.3%, 91.9 and 84.3% respectively. Conclusion: There was a significant linear relation between number of high temperature days and number of heat stroke cases in Pudong New Area. The related work arrangement for heat stroke prevention could be well planed according to the prediction model.
目的： 了解上海市浦东新区高温中暑病例的发生情况，探索高温天气与中暑人数之间的关系并建立相应的预测模型。 方法： 于2017年12月，对2013~2017年夏季上海市浦东新区医疗机构报告的高温中暑病例开展流行病学调查，收集同期的气象资料并进行统计分析。 结果： 2013~2017年浦东新区医疗机构共计报告中暑病例246例，其中男性173例（占70.3%）；重症中暑病例170例（占69.1%）。患者死亡28例，占全部中暑病例的11.4%，占重症中暑病例的16.5%。重症中暑类型以热射病为主（占56.5%）；热衰竭、热痉挛以及混合型分别占17.1%、12.4%和14.0%。散点图显示，年累计高温天数与年中暑人数呈明显的线性关系，线性回归分析结果显示，年累计高温天数与年中暑人数有相关统计学意义（P<0.01）。相应的预测模型为：年中暑人数=β×年累计高温天数+ Intercept。留一法交叉验证结果显示，2013~2017年各年度浦东新区根据最高气温预测高温中暑人数的准确度分别为85.7%、90.9%、83.3%、91.9%、84.3%。 结论： 浦东新区高温中暑病例的数量与当年累计高温天数呈明显线性比例关系，可以根据天气信息预测高温中暑病例的人群数量，为做好相应的防控部署工作减少职业性中暑提供科学的指导依据。.
Keywords: explore; heat stroke; high temperature days; prediction model.