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温度和降雨对赞比亚卢萨卡2003 - 2006年霍乱流行演变的影响:时间序列分析

Influence of temperature and rainfall on the evolution of cholera epidemics in Lusaka, Zambia, 2003-2006: analysis of a time series.

作者信息

Luque Fernández Miguel Angel, Bauernfeind Ariane, Jiménez Julio Díaz, Gil Cristina Linares, El Omeiri Nathalie, Guibert Dionisio Herrera

机构信息

National Centre of Epidemiology (CNE), Programa de Epidemiología Aplicada de Campo, Instituto de Salud Carlos III, C/Sinesio Delgado 6, Pabellón 12, 28029 Madrid, Spain.

出版信息

Trans R Soc Trop Med Hyg. 2009 Feb;103(2):137-43. doi: 10.1016/j.trstmh.2008.07.017. Epub 2008 Sep 9.

Abstract

In this study, we aimed to describe the evolution of three cholera epidemics that occurred in Lusaka, Zambia, between 2003 and 2006 and to analyse the association between the increase in number of cases and climatic factors. A Poisson autoregressive model controlling for seasonality and trend was built to estimate the association between the increase in the weekly number of cases and weekly means of daily maximum temperature and rainfall. All epidemics showed a seasonal trend coinciding with the rainy season (November to March). A 1 degrees C rise in temperature 6 weeks before the onset of the outbreak explained 5.2% [relative risk (RR) 1.05, 95% CI 1.04-1.06] of the increase in the number of cholera cases (2003-2006). In addition, a 50 mm increase in rainfall 3 weeks before explained an increase of 2.5% (RR 1.02, 95% CI 1.01-1.04). The attributable risks were 4.9% for temperature and 2.4% for rainfall. If 6 weeks prior to the beginning of the rainy season an increase in temperature is observed followed by an increase in rainfall 3 weeks later, both exceeding expected levels, an increase in the number of cases of cholera within the following 3 weeks could be expected. Our explicative model could contribute to developing a warning signal to reduce the impact of a presumed cholera epidemic.

摘要

在本研究中,我们旨在描述2003年至2006年间在赞比亚卢萨卡发生的三次霍乱疫情的演变情况,并分析病例数增加与气候因素之间的关联。构建了一个控制季节性和趋势的泊松自回归模型,以估计每周病例数增加与每日最高气温和降雨量的每周均值之间的关联。所有疫情均呈现出与雨季(11月至次年3月)一致的季节性趋势。疫情暴发前6周温度每升高1摄氏度,可解释霍乱病例数增加的5.2%[相对风险(RR)1.05,95%置信区间1.04 - 1.06](2003 - 2006年)。此外,暴发前3周降雨量每增加50毫米,可解释病例数增加2.5%(RR 1.02,95%置信区间1.01 - 1.04)。温度的归因风险为4.9%,降雨的归因风险为2.4%。如果在雨季开始前6周观察到温度升高,随后3周降雨量增加,且两者均超过预期水平,那么在接下来的3周内预计霍乱病例数将会增加。我们的解释模型可能有助于开发一种预警信号,以减轻假定的霍乱疫情的影响。

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