Seasonality of cryptosporidiosis: A meta-analysis approach.
Seasonality of cryptosporidiosis: A meta-analysis approach.
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DOI：
10.1016/j.envres.2009.02.008
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发表时间：
2009-05
影响因子：
8.3
通讯作者：
Naumova, Elena N.
中科院分区：
文献类型：
--
作者：
Jagai, Jyotsna S.;Castronovo, Denise A.;Monchak, Jim;Naumova, Elena N.
关键词：
我们开发了一种方法，并进行了一项荟萃分析，以研究隐孢子虫病的季节性模式，主要是水传播的肠道疾病，与全球降水和温度波动有关。每月隐孢子虫病的数据是从61个已发表的流行病学研究中提取的，这些研究覆盖了基于柯本气候分类的各个气候区域。每个研究地点的每月汇总环境温度和降水量补充了结果数据。我们应用线性混合效应模型将每月的标准化隐孢子虫病发病率与标准化特定位置的温度和降水数据联系起来。我们还进行了归一化植被指数（NDVI），温度和降水对植被的综合影响的遥感措施之间的关联的子分析，并在撒哈拉以南非洲隐孢子虫病。总的来说，在调整了与赤道的距离后，温度和降水的增加预示着隐孢子虫病的增加;关系的强度因气候亚类而异。在潮湿的热带地区，降水是隐孢子虫病的一个强有力的季节性驱动因素，而温度是在中纬度和温带气候。在评估滞后关系时，温度和降水仍然是强有力的预测因素。在撒哈拉以南非洲，在调整了与赤道的距离后，较低的NDVI值预示着下一个月隐孢子虫病的增加。在这项研究中，我们提出了一种新的方法来评估全球范围内疾病结果和气象数据之间的关系。我们的研究结果表明，虽然气候条件通常定义病原体栖息地，但气象因素会影响季节性爆发的时间和强度。因此，气象预报可用于制定水传播隐孢子虫病的重点预防计划。
We developed methodology for and conducted a meta-analysis to examine how seasonal patterns of cryptosporidiosis, a primarily waterborne diarrheal illness, relate to precipitation and temperature fluctuations worldwide. Monthly cryptosporidiosis data were abstracted from 61 published epidemiological studies that cover various climate regions based on the Köppen Climate Classification. Outcome data was supplemented with monthly aggregated ambient temperature and precipitation for each study location. We applied a linear mixed-effects model to relate the monthly normalized cryptosporidiosis incidence with normalized location-specific temperature and precipitation data. We also conducted a sub-analysis of associations between the Normalized Difference Vegetation Index (NDVI), a remote sensing measure for the combined effect of temperature and precipitation on vegetation, and cryptosporidiosis in Sub-Saharan Africa. Overall, and after adjusting for distance from the equator, an increase in temperature and precipitation predict an increase in cryptosporidiosis; the strength of relationship vary by climate subcategory. In moist tropical locations precipitation is a strong seasonal driver for cryptosporidiosis whereas temperature is in mid-latitude and temperate climates. When assessing lagged relationships temperature and precipitation remain strong predictors. In Sub-Saharan Africa, after adjusting for distance from the equator, low NDVI values are predictive of an increase in cryptosporidiosis in the following month. In this study we propose novel methodology to assess relationships between disease outcomes and meteorological data on a global scale. Our findings demonstrate that while climatic conditions typically define a pathogen habitat area, meteorological factors affect timing and intensity of seasonal outbreaks. Therefore, meteorological forecasts can be utilized to develop focused prevention programs for waterborne cryptosporidiosis.
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影响因子：
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作者：
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发表时间：
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