Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Nutrition & Clinical Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Dhaka, Bangladesh.
Environ Res. 2018 Aug;165:91-109. doi: 10.1016/j.envres.2018.02.027. Epub 2018 Apr 21.
Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest.
Standard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products - the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates.
The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern. They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true.
Researchers wishing to utilize publicly available climate data - whether EO-derived or station based - are advised to recognize their specific limitations both in the analysis and the interpretation of the results. Epidemiologists engaged in prospective research into environmentally driven diseases should install their own weather monitoring stations at their study sites whenever possible, in order to circumvent the constraints of choosing between distant or incomplete station data or unverified EO estimates.
需要进行纵向和时间序列分析，以描述水文气象参数与健康结果之间的关联。卫星衍生的地球观测 (EO) 气候数据产品和基于全球模型的再分析产品有可能在气象站观测不足或不完整的情况下和地点用作替代物。然而，这些产品在具有流行病学意义的特定地点往往缺乏直接评估。
将相关性、一致性、偏差和误差的标准评估指标应用于从两个准全球、常用气候数据产品（全球陆面数据同化系统 (GLDAS) 和气候危害组红外降水与站 (CHIRPS)）中提取的十组水文气象变量，以评估其相对于特定地理位置的气象站衍生估计值的性能在多地点队列研究的八个地点的特定地理位置。这些指标是针对每日估计值和 7 天平均值以及轮状病毒高峰期季节子集计算的。然后，将两个来源的变量分别用作队列中轮状病毒感染的纵向回归模型中的预测因子，以调整协变量后测试它们与轮状病毒感染的关联。
基于站点的验证数据的可用性和完整性取决于变量和研究地点。根据不同的评估标准和与完整数据集相比的高峰期季节，同一地点内和不同地点之间的两个网格化气候模型的性能差异很大，其表现方式没有明显的模式。它们与轮状病毒结果的关联的统计显着性也不同。对于某些变量，基于站点的记录显示出很强的关联，而 EO 衍生的估计值则没有，而对于其他变量，则相反。
希望利用公共可用气候数据（无论是 EO 衍生的还是基于站点的）的研究人员建议在分析和解释结果时认识到它们的具体局限性。从事环境驱动疾病前瞻性研究的生态学家应尽可能在其研究地点安装自己的气象监测站，以避免在选择遥远或不完整的站点数据或未经验证的 EO 估计值之间做出选择的限制。
