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本文引用的文献

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The effects of night-time warming on mortality burden under future climate change scenarios: a modelling study.夜间升温对未来气候变化情景下死亡负担的影响:一项建模研究。
Lancet Planet Health. 2022 Aug;6(8):e648-e657. doi: 10.1016/S2542-5196(22)00139-5.
2
Epidemiology and costs of dengue in Brazil: a systematic literature review.巴西登革热的流行病学和成本:系统文献回顾。
Int J Infect Dis. 2022 Sep;122:521-528. doi: 10.1016/j.ijid.2022.06.050. Epub 2022 Jul 3.
3
Fixed-Effect vs Random-Effects Models for Meta-Analysis: 3 Points to Consider.Meta分析的固定效应模型与随机效应模型:三点需考虑之处。
Global Spine J. 2022 Sep;12(7):1624-1626. doi: 10.1177/21925682221110527. Epub 2022 Jun 20.
4
Weather Factors Associated with Reduced Risk of Dengue Transmission in an Urbanized Tropical City.与城市化热带城市登革热传播风险降低相关的气象因素。
Int J Environ Res Public Health. 2021 Dec 29;19(1):339. doi: 10.3390/ijerph19010339.
5
Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.基于真实世界和大数据的登革热预测和监测的数据驱动方法:系统评价。
PLoS Negl Trop Dis. 2022 Jan 7;16(1):e0010056. doi: 10.1371/journal.pntd.0010056. eCollection 2022 Jan.
6
The impacts of precipitation patterns on dengue epidemics in Guangzhou city.降水模式对广州市登革热疫情的影响。
Int J Biometeorol. 2021 Nov;65(11):1929-1937. doi: 10.1007/s00484-021-02149-2. Epub 2021 Jun 11.
7
Population mobility, demographic, and environmental characteristics of dengue fever epidemics in a major city in Southeastern Brazil, 2007-2015.2007-2015 年巴西东南部主要城市登革热流行的人口流动、人口统计学和环境特征。
Cad Saude Publica. 2021 Apr 16;37(4):e00079620. doi: 10.1590/0102-311X00079620. eCollection 2021.
8
Multilevel analysis of social, climatic and entomological factors that influenced dengue occurrence in three municipalities in Colombia.对影响哥伦比亚三个城市登革热发病情况的社会、气候和昆虫学因素的多水平分析。
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9
Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study.水灾害和城市化对巴西登革热风险的综合影响:时空建模研究。
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[气温与登革热发病率之间的关系:巴西米纳斯吉拉斯州的时间序列研究(2010 - 2019年)]

[Relationship between air temperature and dengue incidence: time series study in Minas Gerais, Brazil (2010-2019)].

作者信息

Gomes João Pedro Medeiros, Ribas Igor Magaton, Valadares Pedro Augusto Rosa, Jardim Lucas Santos, Nogueira Mário Círio, Ferreira Cássia de Castro Martins, Watanabe Aripuanã Sakurada Aranha, Ferreira Letícia de Castro Martins

机构信息

Faculdade de Medicina, Universidade Federal de Juiz de Fora, Juiz de Fora, Brasil.

Instituto de Ciências Humanas, Universidade Federal de Juiz de Fora, Juiz de Fora, Brasil.

出版信息

Cad Saude Publica. 2024 Mar 25;40(3):e00076723. doi: 10.1590/0102-311XPT076723. eCollection 2024.

DOI:10.1590/0102-311XPT076723
PMID:38536977
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10962433/
Abstract

Air temperature is a climatic factor that affects the incidence of dengue, with effects varying according to time and space. We investigated the relationship between minimum air temperature and dengue incidence in Minas Gerais, Brazil, and evaluated the influence of socioeconomic and geographic variables on this relationship. This is a time series study with analysis conducted in three distinct stages: modeling using a distributed lag non-linear model, meta-analysis of models obtained, and meta-regression with geographic and socioeconomic data. Minimum temperature was a protective factor at extreme cold temperatures (RR = 0.65; 95%CI: 0.56-0.76) and moderate cold temperatures (RR = 0.71; 95%CI: 0.64-0.79), and a risk factor at moderate hot temperatures (RR = 1.15; 95%CI: 1.07-1.24), but not at extreme hot temperatures (RR = 1.1; 95%CI: 0.99-1.22). Heterogeneity of the models was high (I2 = 60%), which was also observed in meta-regression. Moderate and extreme cold temperatures have a protective effect, while moderate hot temperatures increase the risk. However, minimum air temperature does not explain the variability in the region, not even with the other variables in meta-regression.

摘要

气温是影响登革热发病率的一个气候因素,其影响会因时间和空间而有所不同。我们研究了巴西米纳斯吉拉斯州最低气温与登革热发病率之间的关系,并评估了社会经济和地理变量对这种关系的影响。这是一项时间序列研究,分析分三个不同阶段进行:使用分布滞后非线性模型进行建模、对所得模型进行荟萃分析以及结合地理和社会经济数据进行荟萃回归。最低温度在极端寒冷温度(相对风险 = 0.65;95%置信区间:0.56 - 0.76)和中度寒冷温度(相对风险 = 0.71;95%置信区间:0.64 - 0.79)时是一个保护因素,在中度炎热温度(相对风险 = 1.15;95%置信区间:1.07 - 1.24)时是一个风险因素,但在极端炎热温度时不是(相对风险 = 1.1;95%置信区间:0.99 - 1.22)。模型的异质性较高(I² = 60%),在荟萃回归中也观察到了这一点。中度和极端寒冷温度具有保护作用,而中度炎热温度会增加风险。然而,最低气温并不能解释该地区的变异性,即使在荟萃回归中加入其他变量也不行。