Thomson Madeleine C, Molesworth Anna M, Djingarey Mamoudou H, Yameogo K R, Belanger Francois, Cuevas Luis E
International Research Institute for Climate and Society, The Earth Institute of Columbia University, Palisades, NY 10964, USA.
Trop Med Int Health. 2006 Jun;11(6):781-8. doi: 10.1111/j.1365-3156.2006.01630.x.
Meningococcal meningitis is a major public health problem in Africa. This report explores the potential for climate/environmental models to predict the probability of occurrence of meningitis epidemics.
Time series of meningitis cases by month and district were obtained for Burkina Faso, Niger, Mali and Togo (536 district-years). Environmental information (1989-1999) for the continent [soil and land-cover type, aerosol index, vegetation greenness (NDVI), cold cloud duration (CCD) and rainfall] was used to develop models to predict the incidence of meningitis. Meningitis incidence, dust, rainfall, NDVI and CCD were analysed as anomalies (mean minus observed value). The models were developed using univariate and stepwise multi-variate linear regression.
Anomalies in annual meningitis incidence at district level were related to monthly climate anomalies. Significant relationships were found for both estimates of rainfall and dust in the pre-, post- and epidemic season. While present in all land-cover classes these relationships were strongest in savannah areas.
Predicting epidemics of meningitis could be feasible. To fully develop this potential, we require (a) a better understanding of the epidemiological and environmental phenomena underpinning epidemics and how satellite derived climate proxies reflect conditions on the ground and (b) more extensive epidemiological and environmental datasets. Climate forecasting tools capable of predicting climate variables 3-6 months in advance of an epidemic would increase the lead-time available for control strategies. Our increased capacity for data processing; the recent improvements in meningitis surveillance in preparation for the distribution of the impending conjugate vaccines and the development of other early warning systems for epidemic diseases in Africa, favours the creation of these models.
脑膜炎球菌性脑膜炎是非洲的一个主要公共卫生问题。本报告探讨了气候/环境模型预测脑膜炎流行发生概率的潜力。
获取了布基纳法索、尼日尔、马里和多哥（536个地区年）按月份和地区划分的脑膜炎病例时间序列。利用该大陆的环境信息（1989 - 1999年）[土壤和土地覆盖类型、气溶胶指数、植被绿度（归一化植被指数）、冷云持续时间和降雨量]来建立预测脑膜炎发病率的模型。将脑膜炎发病率、沙尘、降雨量、归一化植被指数和冷云持续时间作为异常值（均值减去观测值）进行分析。使用单变量和逐步多变量线性回归建立模型。
地区层面年度脑膜炎发病率的异常值与月度气候异常值相关。在流行前、流行期间和流行后季节，降雨量和沙尘的估计值均存在显著关系。虽然这些关系在所有土地覆盖类别中都存在，但在稀树草原地区最为强烈。
预测脑膜炎流行可能是可行的。为了充分发挥这一潜力，我们需要（a）更好地理解支撑流行的流行病学和环境现象，以及卫星衍生的气候指标如何反映地面状况，（b）更广泛的流行病学和环境数据集。能够在流行前3 - 6个月预测气候变量的气候预测工具将增加可用于控制策略的提前时间。我们数据处理能力的提高；近期为准备即将分发的结合疫苗而在脑膜炎监测方面的改进，以及非洲其他传染病早期预警系统的开发，都有利于创建这些模型。
