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埃塞俄比亚高原地区疟疾疫情的时空同步性。

Spatial synchrony of malaria outbreaks in a highland region of Ethiopia.

机构信息

Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, USA  Health, Development, and Anti-Malaria Association, Addis Ababa, Ethiopia  United States Agency for International Development, Addis Ababa, Ethiopia  USGS Earth Resources Observation and Science Center, Sioux Falls, SD, USA.

出版信息

Trop Med Int Health. 2012 Oct;17(10):1192-201. doi: 10.1111/j.1365-3156.2012.03058.x. Epub 2012 Aug 5.

Abstract

To understand the drivers and consequences of malaria in epidemic-prone regions, it is important to know whether epidemics emerge independently in different areas as a consequence of local contingencies, or whether they are synchronised across larger regions as a result of climatic fluctuations and other broad-scale drivers. To address this question, we collected historical malaria surveillance data for the Amhara region of Ethiopia and analysed them to assess the consistency of various indicators of malaria risk and determine the dominant spatial and temporal patterns of malaria within the region. We collected data from a total of 49 districts from 1999-2010. Data availability was better for more recent years and more data were available for clinically diagnosed outpatient malaria cases than confirmed malaria cases. Temporal patterns of outpatient malaria case counts were correlated with the proportion of outpatients diagnosed with malaria and confirmed malaria case counts. The proportion of outpatients diagnosed with malaria was spatially clustered, and these cluster locations were generally consistent from year to year. Outpatient malaria cases exhibited spatial synchrony at distances up to 300 km, supporting the hypothesis that regional climatic variability is an important driver of epidemics. Our results suggest that decomposing malaria risk into separate spatial and temporal components may be an effective strategy for modelling and forecasting malaria risk across large areas. They also emphasise both the value and limitations of working with historical surveillance datasets and highlight the importance of enhancing existing surveillance efforts.

摘要

为了了解流行地区疟疾的驱动因素和后果,了解疫情是否是由于当地突发事件在不同地区独立出现,还是由于气候波动和其他大规模驱动因素在更大地区同步出现,这一点很重要。为了解决这个问题,我们收集了埃塞俄比亚阿姆哈拉地区的历史疟疾监测数据,并对其进行了分析,以评估疟疾风险的各种指标的一致性,并确定该地区疟疾的主要时空模式。我们共收集了 1999-2010 年来自 49 个区的数据。较近年份的数据可用性更好,并且临床诊断的门诊疟疾病例比确诊的疟疾病例有更多的数据。门诊疟疾病例数的时间模式与诊断为疟疾的门诊病人比例和确诊的疟疾病例数相关。诊断为疟疾的门诊病人比例具有空间聚类性,这些聚类位置通常每年都保持一致。门诊疟疾病例在长达 300 公里的距离上表现出空间同步性,这支持了区域气候变异性是疫情的重要驱动因素的假设。我们的研究结果表明,将疟疾风险分解为单独的时空成分可能是对大面积疟疾风险进行建模和预测的有效策略。它们还强调了利用历史监测数据集的价值和局限性,并突出了加强现有监测工作的重要性。

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Spatial synchrony of malaria outbreaks in a highland region of Ethiopia.埃塞俄比亚高原地区疟疾疫情的时空同步性。
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