Refining the global spatial limits of dengue virus transmission by evidence-based consensus.
Refining the global spatial limits of dengue virus transmission by evidence-based consensus.
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DOI：
10.1371/journal.pntd.0001760
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发表时间：
2012
影响因子：
3.8
通讯作者：
Hay SI
中科院分区：
文献类型：
--
作者：
Brady OJ;Gething PW;Bhatt S;Messina JP;Brownstein JS;Hoen AG;Moyes CL;Farlow AW;Scott TW;Hay SI
登革热的地理分布和强度都是一个日益严重的问题，但目前的全球分布仍然高度不确定。诊断和诊断方法的挑战以及高度可变的国家卫生系统意味着没有单一数据源可以可靠地估计这种疾病的分布。因此，国际卫生组织对国家登革热现状缺乏共识。在这里，我们使用一种新颖的方法汇集了有关登革热发生的所有可用信息，以制作疾病范围的证据共识图，突出显示登革热状况不确定的国家。使用基线方法来评估每个国家的一系列证据。在登革热状况不确定的地区，纳入了其他证据类型，以澄清登革热状况或确认目前尚不清楚。开发了一种评估证据质量和一致性的算法，为每个国家提供证据共识评分。使用这种方法，我们能够生成国家级登革热状况的当代全球地图，该地图指定了确定性的相对衡量标准并确定了现有证据中的差距。这里制作的地图列出了有充分证据表明登革热发生的 128 个国家/地区，其中包括此前被世界卫生组织和/或美国疾病控制中心列为无登革热的 36 个国家。它还确定了我们完整列出的疾病监测需求。这里使用证据共识确定的疾病范围和限制，标志着一项为期五年的研究的开始，该研究旨在推进登革热病毒传播和疾病风险的绘制。第一步的完成使我们能够对面临风险的人口进行初步估计，上限为 39.7 亿人。这个数字将在未来的工作中得到完善。此前绘制登革热病毒传播全球分布图的尝试产生了不同的结果，特别是在非洲，这反映出所报告的登革热病例的诊断和位置信息缺乏准确性。在这项研究中，我们没有排除这些信息较少的观点，而是将它们与其他不同的证据形式一起纳入适当的不确定性。在组装了不同证据类型的综合数据库后，加权评分系统计算出每个国家的“证据共识”，这是在考虑全部证据时连续衡量登革热存在或不存在的确定性。由此产生的地图和分析有助于突出重要的证据差距，这些证据差距构成了当前登革热分布的不确定性。我们还通过纳入基于问卷的回答来展示当地知识的重要性，这有助于增加不确定地区的清晰度。该分析表明，存在/不存在地图不足以突出用于构建它们的证据基础中的不确定性。通过证据共识绘制地图不仅可以鼓励更多的数据纳入，而且还可以更好地说明登革热当前的全球分布。因此，共识图谱对于证据基础不完整或诊断可靠性较差的一系列被忽视的热带疾病来说是理想的选择。
Dengue is a growing problem both in its geographical spread and in its intensity, and yet current global distribution remains highly uncertain. Challenges in diagnosis and diagnostic methods as well as highly variable national health systems mean no single data source can reliably estimate the distribution of this disease. As such, there is a lack of agreement on national dengue status among international health organisations. Here we bring together all available information on dengue occurrence using a novel approach to produce an evidence consensus map of the disease range that highlights nations with an uncertain dengue status. A baseline methodology was used to assess a range of evidence for each country. In regions where dengue status was uncertain, additional evidence types were included to either clarify dengue status or confirm that it is unknown at this time. An algorithm was developed that assesses evidence quality and consistency, giving each country an evidence consensus score. Using this approach, we were able to generate a contemporary global map of national-level dengue status that assigns a relative measure of certainty and identifies gaps in the available evidence. The map produced here provides a list of 128 countries for which there is good evidence of dengue occurrence, including 36 countries that have previously been classified as dengue-free by the World Health Organization and/or the US Centers for Disease Control. It also identifies disease surveillance needs, which we list in full. The disease extents and limits determined here using evidence consensus, marks the beginning of a five-year study to advance the mapping of dengue virus transmission and disease risk. Completion of this first step has allowed us to produce a preliminary estimate of population at risk with an upper bound of 3.97 billion people. This figure will be refined in future work. Previous attempts to map the current global distribution of dengue virus transmission have produced variable results, particularly in Africa, reflecting the lack of accuracy in both diagnostic and locational information of reported dengue cases. In this study, instead of excluding these less informed points we included them with appropriate uncertainty alongside other diverse evidence forms. After assembling a comprehensive database of different evidence types, a weighted scoring system calculated “evidence consensus” for each country a continuous measure of the certainty of dengue presence or absence when considering the full aggregate of evidence. The resulting map and analysis helped highlight important evidence gaps that underlie uncertainties in the current distribution of dengue. We also show the importance of local knowledge through incorporating questionnairebased responses that can help add clarity in uncertain regions. This analysis showed that presence/absence maps do not sufficiently highlight the uncertainties in the evidence base used to construct them. Mapping by evidence consensus not only encourages greater data inclusion, but it also better illustrates the current global distribution of dengue. Consensus mapping is thus ideal for a range of neglected tropical diseases where the evidence base is incomplete or less diagnostically reliable.
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使用Web搜索查询数据监测登革热流行：一种被忽视的热带疾病监测的新模型。
影响因子：
3.8
作者：
Chan EH;Sahai V;Conrad C;Brownstein JS
通讯作者：
Brownstein JS
影响因子：
3
作者：
Gething PW;Patil AP;Smith DL;Guerra CA;Elyazar IR;Johnston GL;Tatem AJ;Hay SI
通讯作者：
Hay SI
影响因子：
15.8
作者：
Brownstein JS;Freifeld CC;Reis BY;Mandl KD
通讯作者：
Mandl KD
影响因子：
11.8
作者：
Amarasinghe A;Kuritsk JN;Letson GW;Margolis HS
通讯作者：
Margolis HS
DOI：
10.1093/bioinformatics/btn534
发表时间：
2008-12-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Collier N;Doan S;Kawazoe A;Goodwin RM;Conway M;Tateno Y;Ngo QH;Dien D;Kawtrakul A;Takeuchi K;Shigematsu M;Taniguchi K
通讯作者：
Taniguchi K