浙江省气温对其他感染性腹泻的滞后效应及影响因素

王海涛; 刘志东; 劳家辉; 赵哲; 姜宝法

文章摘要

王海涛,刘志东,劳家辉,赵哲,姜宝法.浙江省气温对其他感染性腹泻的滞后效应及影响因素[J].中华流行病学杂志,2019,40(8):960-964

浙江省气温对其他感染性腹泻的滞后效应及影响因素

Lag effect and influencing factors of temperature on other infectious diarrhea in Zhejiang province

收稿日期:2019-01-16 出版日期:2019-08-19

DOI：10.3760/cma.j.issn.0254-6450.2019.08.016

中文关键词: 气温其他感染性腹泻两阶段模型

英文关键词: Temperature Other infectious diarrhea Two-stage model

基金项目:国家科技基础资源调查专项（2017FY101202）

作者	单位	E-mail
王海涛	山东大学公共卫生学院流行病学系, 济南 250012
刘志东	山东大学公共卫生学院流行病学系, 济南 250012
劳家辉	山东大学公共卫生学院流行病学系, 济南 250012
赵哲	山东大学公共卫生学院流行病学系, 济南 250012
姜宝法	山东大学公共卫生学院流行病学系, 济南 250012 山东大学气候变化与健康研究中心, 济南 250012	bjiang@sdu.edu.cn

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中文摘要:

目的研究气温对浙江省不同城市其他感染性腹泻的滞后效应，并探讨其异质性来源，找出脆弱人群。方法收集2014-2016年浙江省其他感染性腹泻资料及同期气象资料。采用两阶段模型，首先在各个市利用分布滞后非线性模型评价气温对其他感染性腹泻的滞后效应，然后采用多变量Meta分析合并效应值，再通过Meta回归进一步探索其异质性来源。结果研究期间浙江省共发生其他感染性腹泻301 593例。在全省水平上，其他感染性腹泻发病风险最低时对应的温度为16.7℃，以16.7℃作为参照温度，发病风险最高时对应的温度为6.2℃（RR=2.298，95% CI：1.527~3.459）。以日平均气温的P₅、P₉₅分别代表低温和高温，低温时其他感染性腹泻的发病风险滞后2 d显现，第5天时风险最高（RR=1.057，95% CI：1.030~1.084），然后持续降低至第23天。高温对应的发病风险当天就会出现（RR=1.081，95% CI：1.045~1.118），并逐渐减小至第8天。不同地区其他感染性腹泻发病风险差异的异质性来源有城市纬度及人口老龄化率。结论高温或低温均会增加其他感染性腹泻的发病风险，且存在滞后效应。低温时应加强对高纬度地区人群及老年人群其他感染性腹泻的预防。

英文摘要:

Objective To study the lag effect of temperature and the source of heterogeneity on other infectious diarrhea (OID) in Zhejiang province, so as to identify related vulnerable populations at risk. Methods Data on OID and meteorology in Zhejiang province from 2014 to 2016 were collected. A two-stage model was conducted, including:1) using the distributed lag non-linear model to estimate the city-specific lag effect of temperature on OID, 2) applying the multivariate Meta-analysis to pool the estimated city-specific effect, 3) using the multivariate Meta-regression to explore the sources of heterogeneity. Results There were 301 593 cases of OID in Zhejiang province during the study period. At the provincial level, temperature that corresponding to the lowest risk of OID was 16.7℃, and the temperature corresponding to the highest risk was 6.2℃ (RR=2.298, 95%CI:1.527-3.459). 16.7℃ was recognized as the reference temperature. P₅ and P₉₅ of the average daily temperature represented low and high temperature respectively. When the temperature was cold, the risk was delayed by 2 days, with the highest risk found on the 5^th day (RR=1.057, 95%CI:1.030-1.084) before decreasing to the 23^rd day. When the temperature got hot, the risk of OID occurred on the first day (RR=1.081, 95%CI:1.045-1.118) and gradually decreasing to the 8^th day. Differences on heterogeneous sources related to the risks of OID in different regions, presented on urban latitude and the rate of ageing in the population. Conclusions Both high or low temperature could increase the risk of OID, with a lag effect noticed. Prevention program on OID should be focusing on populations living in the high latitude and the elderly population at the low temperature areas.

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