Buenconsejo Joan, Fish Durland, Childs James E, Holford Theodore R
Center for Drugs, Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Bldg. 22, Rm. 3241, Silver Spring, MD 20993-0002, USA.
Stat Med. 2008 Jul 30;27(17):3269-85. doi: 10.1002/sim.3190.
A model-based approach to analyze two incomplete disease surveillance datasets is described. Such data typically consist of case counts, each originating from a specific geographical area. A Bayesian hierarchical model is proposed for estimating the total number of cases with disease while simultaneously adjusting for spatial variation. This approach explicitly accounts for model uncertainty and can make use of covariates.The method is applied to two surveillance datasets maintained by the Centers for Disease Control and Prevention on Rocky Mountain spotted fever (RMSF). An inference is drawn using Markov Chain Monte Carlo simulation techniques in a fully Bayesian framework. The central feature of the model is the ability to calculate and estimate the total number of cases and disease incidence for geographical regions where RMSF is endemic.The information generated by this model could significantly reduce the public health impact of RMSF and other vector-borne zoonoses, as well as other infectious or chronic diseases, by improving knowledge of the spatial distribution of disease risk of public health officials and medical practitioners. More accurate information on populations at high risk would focus attention and resources on specific areas, thereby reducing the morbidity and mortality caused by some of the preventable and treatable diseases.
本文描述了一种基于模型的方法，用于分析两个不完整的疾病监测数据集。此类数据通常由病例数组成，每个病例数都来自特定的地理区域。提出了一种贝叶斯层次模型，用于估计疾病的病例总数，同时调整空间变异。该方法明确考虑了模型不确定性，并可以利用协变量。该方法应用于疾病控制与预防中心维护的两个关于落基山斑疹热（RMSF）的监测数据集。在完全贝叶斯框架下，使用马尔可夫链蒙特卡罗模拟技术进行推断。该模型的核心特征是能够计算和估计RMSF流行的地理区域的病例总数和疾病发病率。该模型生成的信息可以通过提高公共卫生官员和医生对疾病风险空间分布的了解，显著降低RMSF和其他媒介传播人畜共患病以及其他传染病或慢性病对公共卫生的影响。关于高危人群的更准确信息将把注意力和资源集中在特定区域，从而降低一些可预防和可治疗疾病导致的发病率和死亡率。
