Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China
文献类型：期刊论文
| 作者 | Cheng, Qu; Jing, Qinlong2; Collender, Philip A.; Head, Jennifer R.; Li, Qi; Yu, Hailan; Li, Zhichao5; Ju, Yang4; Chen, Tianmu3; Wang, Peng |
| 刊名 | FRONTIERS IN PUBLIC HEALTH
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 11页码:1287678 |
| 关键词 | dengue heavy rainfall events prior water availability DLNM Guangzhou |
| DOI | 10.3389/fpubh.2023.1287678 |
| 文献子类 | Article |
| 英文摘要 | IntroductionGiven the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier.MethodsIn this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.ResultsOur findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24-55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02-1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7-121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43-0.79), occurring at a lag of 45 days.DiscussionThese findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk. |
| WOS关键词 | SPATIOTEMPORAL PATTERNS ; METEOROLOGICAL FACTORS ; MODELS |
| WOS研究方向 | Public, Environmental & Occupational Health |
| WOS记录号 | WOS:001125717900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200928]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Xiamen Univ, Sch Publ Hlth, State Key Lab Mol Vaccinol & Mol Diagnost, Xiamen, Peoples R China 2.Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Publ Hlth, Dept Epidemiol & Biostat, Wuhan, Peoples R China 3.Nanjing Univ, Sch Architecture & Urban Planning, Nanjing, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China 5.Head, Jennifer R.] Univ Calif Berkeley, Sch Publ Hlth, Div Epidemiol, Berkeley, CA USA 6.Univ Southampton, Sch Geog & Environm Sci, WorldPop, Southampton, England 7.Collender, Philip A.] Univ Calif Berkeley, Sch Publ Hlth, Div Environm Hlth Sci, Berkeley, CA 94720 USA 8.Guangzhou Ctr Dis Control & Prevent, Dept Infect Dis, Guangzhou 510440, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cheng, Qu,Jing, Qinlong,Collender, Philip A.,et al. Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China[J]. FRONTIERS IN PUBLIC HEALTH,2023,11:1287678. |
| APA | Cheng, Qu.,Jing, Qinlong.,Collender, Philip A..,Head, Jennifer R..,Li, Qi.,...&Lai, Shengjie.(2023).Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China.FRONTIERS IN PUBLIC HEALTH,11,1287678. |
| MLA | Cheng, Qu,et al."Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China".FRONTIERS IN PUBLIC HEALTH 11(2023):1287678. |
入库方式： OAI收割
来源：地理科学与资源研究所
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