Jagai Jyotsna S, Castronovo Denise A, Monchak Jim, Naumova Elena N
Department of Public Health and Family Medicine, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA 02111, USA.
Environ Res. 2009 May;109(4):465-78. doi: 10.1016/j.envres.2009.02.008. Epub 2009 Mar 27.
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 were supplemented with monthly aggregated ambient temperature and precipitation for each study location. We applied a linear mixed-effect 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, increases in temperature and precipitation predict an increase in cryptosporidiosis; the strengths 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.
我们开发了相关方法并进行了一项荟萃分析,以研究隐孢子虫病(一种主要通过水传播的腹泻疾病)的季节性模式与全球降水和温度波动之间的关系。
从61项已发表的流行病学研究中提取每月隐孢子虫病数据,这些研究涵盖了基于柯本气候分类的不同气候区域。每个研究地点的每月综合环境温度和降水量数据作为结果数据的补充。我们应用线性混合效应模型将每月标准化隐孢子虫病发病率与特定地点的标准化温度和降水量数据相关联。我们还对归一化植被指数(NDVI,一种用于衡量温度和降水对植被综合影响的遥感指标)与撒哈拉以南非洲地区隐孢子虫病之间的关联进行了亚组分析。
总体而言,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发病率的上升;这种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烈季节性驱动因素,而在中纬度和温带气候地区,温度则是主要驱动因素。在评估滞后关系时,温度和降水仍然是强有力的预测指标。在撒哈拉以南非洲地区,调整与赤道的距离后,低NDVI值预示着下个月隐孢子虫病发病率会上升。
在本研究中,我们提出了一种新的方法来评估全球范围内疾病结果与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了病原体的栖息地范围,但气象因素会影响季节性疫情爆发的时间和强度。因此,气象预报可用于制定针对水传播隐孢子虫病的重点预防计划。