PDF(Pubmed)
Abstract：
Lyme disease is a spatially heterogeneous tick-borne infection, with approximately 85% of US cases concentrated in the mid-Atlantic and northeastern states. Surveillance for Lyme disease and its causative agent, including public health case reporting and entomologic surveillance, is necessary to understand its endemic range, but currently used case detection methods have limitations. To evaluate an alternative approach to Lyme disease surveillance, we have performed a geospatial analysis of Lyme disease cases from the Johns Hopkins Health System in Maryland. We used two sources of cases: a) individuals with both a positive test for Lyme disease and a contemporaneous diagnostic code consistent with a Lyme disease-related syndrome; and b) individuals referred for a Lyme disease evaluation who were adjudicated to have Lyme disease. Controls were individuals from the referral cohort judged not to have Lyme disease. Residential address data were available for all cases and controls. We used a hierarchical Bayesian model with a smoothing function for a coordinate location to evaluate the probability of Lyme disease within 100 km of Johns Hopkins Hospital. We found that the probability of Lyme disease was greatest in the north and west of Baltimore, and the local probability that a subject would have Lyme disease varied by as much as 30-fold. Adjustment for demographic and ecological variables partially attenuated the spatial gradient. Our study supports the suitability of electronic medical record data for the retrospective surveillance of Lyme disease.
摘要：
莱姆病是一种空间异质性蜱传感染，美国约85%的病例集中在大西洋中部和东北部各州。莱姆病及其病原体的监测，包括公共卫生病例报告和昆虫学监测，有必要了解它的流行范围，但目前常用的病例检测方法有局限性。为了评估莱姆病监测的替代方法，我们对马里兰州约翰霍普金斯卫生系统的莱姆病病例进行了地理空间分析。我们使用了两种病例来源：a）对莱姆病的阳性检测和与莱姆病相关综合征一致的同期诊断代码的个体；b）被推荐接受莱姆病评估的个体，被裁定患有莱姆病。对照组是来自转诊队列的被判断为没有莱姆病的个体。住宅地址数据适用于所有病例和对照。我们使用带有平滑函数的分层贝叶斯模型进行坐标位置，以评估约翰霍普金斯医院100公里内莱姆病的概率。我们发现在巴尔的摩北部和西部，莱姆病的可能性最大，受试者患莱姆病的局部概率变化多达30倍。人口和生态变量的调整部分减弱了空间梯度。我们的研究支持电子病历数据对莱姆病回顾性监测的适用性。
