山东大学学报 (医学版) ›› 2025, Vol. 63 ›› Issue (4): 100-105.doi: 10.6040/j.issn.1671-7554.0.2024.0896
张骁驰1,吕婷婷2,于文浩1,李国傲1,高杉杉3,4,赵琦1,王立友2
ZHANG Xiaochi1, LYU Tingting2, YU Wenhao1, LI Guoao1, GAO Shanshan3,4, ZHAO Qi1, WANG Liyou2
摘要: 目的 探讨我国2014—2019年极端降水事件与其他感染性腹泻(other infectious diarrhea, OID)发病的关系,以及气候特征和干旱水平对极端降水相关OID风险的影响,为制定OID防控措施提供依据。 方法 收集2014—2019年我国31个省、直辖市、自治区(无香港、澳门、台湾省)的OID报告病例数据和同期水文气象数据,采用基于类泊松回归的二阶段时间序列分析方法量化极端降水事件频次与OID发病风险的关联;采用交互模型研究自然气候区及6个月标准化降水蒸散指数量化的干旱水平的修饰作用。 结果 2014—2019年研究区域内累计报告OID病例5 595 698例;极端降水事件频次与OID风险呈显著正相关,相对危险度为1.03(95%CI:1.03~1.04);暖温带半湿润地区极端降水相关的OID风险最高;北亚热带湿润地区、边缘热带湿润地区以及中温带干旱地区的极端降水相关OID风险显著低于暖温带半湿润地区(P<0.05)。干旱水平对极端降水的修饰作用明显;严重干旱地区在面临极端降水时的OID风险较高。 结论 极端降水频次与OID发病风险呈正相关,可作为不同地区制定针对性预防措施的依据。
中图分类号:
| [1] Li L, Sun MX, Zhao J. Other infectious diarrhea[M] //Radiology of infectious diseases: Volume 2. Dordrecht: Netherlands, 2015: 171-186. [2] 汤家炜, 汤其宁, 朱时雨, 等. 2015—2019年中国法定传染病发病趋势分析 [J]. 医学动物防制, 2024, 40(1): 4-7. TANG Jiawei, TANG Qining, ZHU Shiyu, et al. Analysis of the incidence trend of notifiable infectious diseases in China from 2015 to 2019[J]. Journal of Medical Pest Control, 2024, 40(1): 4-7. [3] Baral R, Nonvignon J, Debellut F, et al. Cost of illness for childhood diarrhea in low- and middle-income countries: a systematic review of evidence and modelled estimates[J]. BMC Public Health, 2020, 20(1): 619. doi:10.1186/s12889-020-08595-8 [4] 劳家辉, 刘志东, 刘言玉, 等. 成都市暴雨洪涝灾害对其它感染性腹泻发病影响及脆弱人群分析[J]. 中国公共卫生, 2019, 35(8): 1046-1049. LAO Jiahui, LIU Zhidong, LIU Yanyu, et al. Influence of floods on incidence of other infectious diarrhea and vulnerable population in Chengdu City[J]. Chinese Journal of Public Health, 2019, 35(8): 1046-1049. [5] 高璐. 暴雨洪涝相关敏感传染病的筛选及预估研究: 以安徽省为例[D]. 济南: 山东大学, 2016. [6] 李燃. 气象因素所致感染性腹泻关联分析与风险评估[D]. 成都: 成都医学院, 2023. [7] Wang P, Asare EO, Pitzer VE, et al. Floods and diarrhea risk in young children in low- and middle-income countries[J]. JAMA Pediatr, 2023, 177(11): 1206-1214. [8] Calvin K, Dasgupta D, Krinner G, et al. Climate change 2023: synthesis report, summary for policymakers. contribution of working groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change [R]. Geneva: IPCC, 2023: 1-34. [9] Kraay ANM, Man O, Levy MC, et al. Understanding the impact of rainfall on diarrhea: testing the concentration-dilution hypothesis using a systematic review and meta-analysis[J]. Environ Health Perspect, 2020, 128(12): 126001. doi:10.1289/EHP6181 [10] Wang LC, Wang JH, He F, et al. Spatial-temporal variation of extreme precipitation in the Yellow-Huai-Hai-Yangtze Basin of China[J]. Sci Rep, 2023, 13: 9312. doi:10.1038/s41598-023-36470-0 [11] 中华人民共和国卫生部. 感染性腹泻诊断标准: WS 271—2007[S]. 北京: 人民卫生出版社, 2007. [12] 吴佳, 高学杰. 一套格点化的中国区域逐日观测资料及与其它资料的对比[J]. 地球物理学报, 2013, 56(4): 1102-1111. WU Jia, GAO Xuejie. A gridded daily observation dataset over China region and comparison with the other datasets[J]. Chinese Journal of Geophysics, 2013, 56(4): 1102-1111. [13] 中华人民共和国自然资源部. 国家地理信息公共服务平台 [EB/OL].(2024-04-24)[2024-07-15]. https://www.tianditu.gov.cn/ [14] 赵松乔.中国综合自然地理区划的一个新方案[J].地理学报,1983, 38(1): 1-10. ZHAO Songqiao. A new scheme for comprehensive physical regionalization in China[J]. Acta Geographica Sinica, 1983, 38(1): 1-10. [15] Zhang XB, Alexander L, Hegerl GC, et al. Indices for monitoring changes in extremes based on daily temperature and precipitation data[J]. Wires Clim Change, 2011, 2(6): 851-870. [16] Myhre G, Alterskjær K, Stjern CW, et al. Frequency of extreme precipitation increases extensively with event rareness under global warming[J]. Sci Rep, 2019, 9(1): 16063. doi:10.1038/s41598-019-52277-4 [17] 中华人民共和国国家质量监督检验检疫总局. 气象干旱等级: GB/T 20481-2017[S]. 北京:中国标准出版社, 2017. [18] Wang P, Asare E, Pitzer VE, et al. Associations between long-term drought and diarrhea among children under five in low- and middle-income countries[J]. Nat Commun, 2022, 13(1): 3661. doi:10.1038/s41467-022-31291-7 [19] Balting DF, AghaKouchak A, Lohmann G, et al. Northern Hemisphere drought risk in a warming climate[J]. NPJ Clim Atmos Sci, 2021, 4: 61. doi:10.1038/s41612-021-00218-2 [20] Gasparrini A, Armstrong B, Kenward MG. Multivariate meta-analysis for non-linear and other multi-parameter associations[J]. Stat Med, 2012, 31(29): 3821-3839. [21] 郝强, 高琦, 赵然, 等. 2014—2016年气温和相对湿度对深圳市5岁以下儿童轮状病毒腹泻的影响[J]. 山东大学学报(医学版), 2022, 60(2): 89-95. HAO Qiang, GAO Qi, ZHAO Ran, et al. Effects of ambient temperature and relative humidity on rotavirus diarrhea among children under 5 years old in Shenzhen City during 2014-2016[J]. Journal of Shandong University(Health Sciences), 2022, 60(2): 89-95. [22] Altman DG, Martin Bland J. Interaction revisited: the difference between two estimates[J]. BMJ, 2003, 326(7382): 219. doi:10.1136/bmj.326.7382.219 [23] 姜宝法, 丁国永, 刘雪娜. 暴雨洪涝与人类健康关系的研究进展[J]. 山东大学学报(医学版), 2018, 56(8): 21-28. JIANG Baofa, DING Guoyong, LIU Xuena. Research progress on the relationship between floods and human health[J]. Journal of Shandong University(Health Science), 2018, 56(8): 21-28. [24] Du SC, Chien LC, Bush KF, et al. Short-term associations between precipitation and gastrointestinal illness-related hospital admissions: a multi-city study in Texas[J]. Sci Total Environ, 2024, 951: 175247. doi:10.1016/j.scitotenv.2024.175247 [25] Mertens A, Balakrishnan K, Ramaswamy P, et al. Associations between high temperature, heavy rainfall, and diarrhea among young children in rural Tamil Nadu, India: a prospective cohort study[J]. Environ Health Perspect, 2019, 127(4): 47004. doi:10.1289/EHP3711 [26] Bradatan C, Dennis JA, Flores-Yeffal N, et al. Child health, household environment, temperature and rainfall anomalies in Honduras: a socio-climate data linked analysis[J]. Environ Health, 2020, 19(1): 10. doi:10.1186/s12940-020-0560-9 [27] 朱耿睿, 李育. 基于柯本气候分类的1961—2013年我国气候区类型及变化[J]. 干旱区地理, 2015, 38(6): 1121-1132. ZHU Gengrui, LI Yu. Types and changes of Chinese climate zones from 1961 to 2013 based on Köppen climate classification[J]. Arid Land Geography, 2015, 38(6): 1121-1132. [28] 张静. 2014—2016年降水和温度对北京市其他感染性腹泻的影响[D]. 济南: 山东大学, 2019. [29] 周士夏, 张海洋, 王丽萍, 等. 基于分布滞后非线性模型探讨上海市诺如病毒感染性腹泻的发病与气象因素的关联[J]. 中华疾病控制杂志, 2021, 25(10): 1180-1185. ZHOU Shixia, ZHANG Haiyang, WANG Liping, et al. Exploration of the association between meteorological factors and positive rate of norovirus infectious diarrhea based on the distributed lag non-linear model in Shanghai[J]. Chinese Journal of Disease Control & Prevention, 2021, 25(10): 1180-1185. [30] Dimitrova A, McElroy S, Levy M, et al. Precipitation variability and risk of infectious disease in children under 5 years for 32 countries: a global analysis using demographic and health survey data[J]. Lancet Planet Health, 2022, 6(2): e147-e155. [31] Yu JF, Zhao L, Liang XZ, et al. The mediatory role of water quality on the association between extreme precipitation events and infectious diarrhea in the Yangtze River Basin, China[J]. Fundam Res, 2024, 4(3): 495-504. [32] Wu XX, Lu YM, Zhou S, et al. Impact of climate change on human infectious diseases: empirical evidence and human adaptation[J]. Environ Int, 2016, 86: 14-23. doi:10.1016/j.envint.2015.09.007 [33] 杨廉平, 刘立, 刘雨晨, 等. 暴雨洪涝影响感染性腹泻发病的环境-社会因素与社会驱动过程模型构建[J]. 环境与职业医学, 2022, 39(3): 296-303. YANG Lianping, LIU Li, LIU Yuchen, et al. Review on environmental-social factors and social driving process model construction of infectious diarrhea affected by rainstorm and flood[J]. Journal of Environmental and Occupational Medicine, 2022, 39(3): 296-303. |
| [1] | 李欣怡,张骁驰,李文,高杉杉,赵琦,张玮. 2018—2022年5~10月山东省热浪与学龄人群其他感染性腹泻发病的关联研究[J]. 山东大学学报 (医学版), 2025, 63(3): 110-116. |
| [2] | 钱凤同,李洪凯,于金龙,薛付忠. 抗菌药物使用密度与肺炎克雷伯菌耐药率的因果关联及药物控制阈值[J]. 山东大学学报 (医学版), 2024, 62(5): 103-111. |
| [3] | 李传玺,刘起勇,马伟. 广州市极端降水事件对不同特征人群登革热发病的影响[J]. 山东大学学报 (医学版), 2021, 59(12): 151-157. |
| [4] | 陈浪,赵川,陈凤格,白萍. 石家庄市大气颗粒污染物浓度对儿童呼吸系统疾病门诊量的影响[J]. 山东大学学报 (医学版), 2018, 56(11): 68-75. |
| [5] | 曹若明,崔亮亮,姜超,景一鸣,周林,张琳,刘守钦. 济南市大气污染物O3与居民呼吸系统疾病死亡风险的时间序列分析[J]. 山东大学学报 (医学版), 2018, 56(11): 91-97. |
| [6] | 满金宇,岳克三,崔亮亮,李新伟,韩联宇,吴兴彬,刘守钦. 2014~2016年济南市历城区大气气态污染物对社区人群门诊就诊影响的时间序列分析[J]. 山东大学学报 (医学版), 2018, 56(11): 98-104. |
| [7] | 肖长春,唐静,李玉荣,翟金霞. 合肥市空气污染与某儿童医院肺炎门诊量关系的时间序列分析[J]. 山东大学学报 (医学版), 2018, 56(11): 76-83. |
| [8] | 贾晓倩,崔亮亮,岳克三,李新伟,韩联宇,吴兴彬,周敬文. 济南市大气颗粒物与儿童呼吸系统疾病就诊量的时间序列分析[J]. 山东大学学报 (医学版), 2018, 56(11): 84-90. |
| [9] | 崔亮亮,张萌,于坤坤,姜超,阮师漫. 济南市大气重点污染物对居民应急呼叫事件的急性影响[J]. 山东大学学报 (医学版), 2018, 56(11): 34-41. |
| [10] | 满金宇,崔亮亮,韩联宇,于坤坤,吴兴彬,岳克三,周敬文. 2014~2016年济南市空气污染严重地区大气颗粒物对社区人群门诊就诊量的急性效应分析[J]. 山东大学学报 (医学版), 2018, 56(11): 61-67. |
| [11] | 刘晓利,刘芳盈,孟超,李平,张殿平,殷茂荣,翟慎永. 淄博市大气污染物浓度与急救人次关联的时间序列分析[J]. 山东大学学报 (医学版), 2018, 56(11): 42-47. |
| [12] | 李润滋,章涛,梁玉民,罗成,蒋正,薛付忠,刘言训,刘静,李秀君. SARIMA模型在流行性腮腺炎发病预测中的应用[J]. 山东大学学报(医学版), 2016, 54(9): 82-86. |
|