Relative risk estimation of dengue disease at small spatial scale.
Relative risk estimation of dengue disease at small spatial scale.
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DOI：
10.1186/s12942-017-0104-x
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发表时间：
2017-08-15
影响因子：
4.9
通讯作者：
Torres Prieto A
中科院分区：
文献类型：
--
作者：
Martínez-Bello DA;López-Quílez A;Torres Prieto A
登革热是世界各地热带国家的一种高发虫媒病毒性疾病。由于有利于媒介生存和传播的环境条件，哥伦比亚是一个流行国家。哥伦比亚的登革热监测以被动通报病例为基础，支持监测、预测、风险因素识别和干预措施。尽管监测网络运作良好，但目前针对许多健康问题开发和使用的疾病绘图技术并未得到广泛应用。我们选择哥伦比亚布卡拉曼加市应用贝叶斯区域疾病绘图模型，测试该方法的挑战和困难。我们按人口普查分区（大约由 1-20 个城市街区组成的地理单位）估算了 2008 年 1 月至 2015 年 12 月期间登革热疾病的相对风险。我们纳入了通过卫星图像获得的协变量归一化植被指数 (NDVI) 和地表温度 (LST)。我们使用马尔可夫链蒙特卡罗模拟，在 2008-2015 年的完整周期和年度聚合时间尺度上拟合贝叶斯区域模型，协变量具有固定系数和空间变化系数。此外，我们还使用 Cohen 的 Kappa 协议度量来比较逐年以及逐年到完整期间汇总的风险。我们发现，在估计登革热相对风险方面，NDVI 比 LST 提供了更多信息，尽管它们的影响很小。 NDVI 与登革热的高相对风险直接相关。登革热风险图是根据建模过程获得的估计值生成的。人口普查部门的逐年风险协议不太公平。该研究提供了一个使用贝叶斯模型实施相对风险估计的示例，用于小空间尺度的协变量疾病绘图。我们使用区域数据方法将卫星数据与登革热疾病联系起来，这在文献中并不常见。该研究的主要困难是找到用于生成预期值作为模型输入的高质量数据。我们指出在小空间尺度上建立人口登记册的重要性，这不仅与登革热的风险评估相关，而且对于所有法定疾病的监测也很重要。
Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach. We estimated the relative risk of dengue disease by census section (a geographical unit composed approximately by 1–20 city blocks) for the period January 2008 to December 2015. We included the covariates normalized difference vegetation index (NDVI) and land surface temperature (LST), obtained by satellite images. We fitted Bayesian areal models at the complete period and annual aggregation time scales for 2008–2015, with fixed and space-varying coefficients for the covariates, using Markov Chain Monte Carlo simulations. In addition, we used Cohen’s Kappa agreement measures to compare the risk from year to year, and from every year to the complete period aggregation. We found the NDVI providing more information than LST for estimating relative risk of dengue, although their effects were small. NDVI was directly associated to high relative risk of dengue. Risk maps of dengue were produced from the estimates obtained by the modeling process. The year to year risk agreement by census section was sligth to fair. The study provides an example of implementation of relative risk estimation using Bayesian models for disease mapping at small spatial scale with covariates. We relate satellite data to dengue disease, using an areal data approach, which is not commonly found in the literature. The main difficulty of the study was to find quality data for generating expected values as input for the models. We remark the importance of creating population registry at small spatial scale, which is not only relevant for the risk estimation of dengue but also important to the surveillance of all notifiable diseases.
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