Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montreal, Quebec H3A 1A2, Canada; Département de Médecine Sociale et Préventive, École de Santé Publique, Université de Montréal. Montreal, Quebce, Canada.
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montreal, Quebec H3A 1A2, Canada.
Spat Spatiotemporal Epidemiol. 2022 Jun;41:100495. doi: 10.1016/j.sste.2022.100495. Epub 2022 Mar 3.
The spatial distribution of surveillance-reported dengue cases and severity are usually analyzed separately, assuming independence between the spatial distribution of non-severe and severe cases. Given the availability of data for the individual geo-location of surveillance-notified dengue cases, we conducted a spatial analysis to model non-severe and severe dengue simultaneously, using a hierarchical Bayesian model. We fit a joint model to the spatial pattern formed by dengue cases as well as to the severity status of the cases. Results showed that age and socioeconomic status were associated with dengue presence, and there was evidence of clustering for overall cases but not for severity. Our findings inform decision making to address the preparedness or implementation of dengue control strategies at the local level.
监测报告的登革热病例和严重程度的空间分布通常是分开分析的,假设非严重和严重病例的空间分布是独立的。鉴于监测通知的登革热病例的个别地理位置数据可用,我们使用分层贝叶斯模型进行了空间分析,以同时对非严重和严重登革热进行建模。我们拟合了一个联合模型,以模拟登革热病例形成的空间模式以及病例的严重程度。结果表明,年龄和社会经济地位与登革热的存在有关,而且总体病例存在聚集现象,但严重程度没有聚集现象。我们的研究结果为在地方一级制定或实施登革热控制策略提供了决策依据。