Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Epidemiology and Health Services Research, Geisinger, Danville, PA, USA.
Environ Res. 2019 Nov;178:108649. doi: 10.1016/j.envres.2019.108649. Epub 2019 Aug 13.
Land use and forest fragmentation are thought to be major drivers of Lyme disease incidence and its geographic distribution. We examined the association between landscape composition and configuration and Lyme disease in a population-based case control study in the Geisinger health system in Pennsylvania. Lyme disease cases (n = 9657) were identified using a combination of diagnosis codes, laboratory codes, and antibiotic orders from electronic health records (EHRs). Controls (5:1) were randomly selected and frequency matched on year, age, and sex. We measured six landscape variables based on prior literature, derived from the National Land Cover Database and MODIS satellite imagery: greenness (normalized difference vegetation index), percent forest, percent herbaceous, forest edge density, percent forest-herbaceous edge, and mean forest patch size. We assigned landscape variables within two spatial contexts (community and ½-mile [805 m] Euclidian residential buffer). In models stratified by community type, landscape variables were modeled as tertiles and flexible splines and associations were adjusted for demographic and clinical covariates. In general, we observed positive associations between landscape metrics and Lyme disease, except for percent herbaceous, where associations differed by community type. For example, compared to the lowest tertile, individuals with highest tertile of greenness in residential buffers had higher odds of Lyme disease (odds ratio: 95% confidence interval [CI]) in townships (1.73: 1.55, 1.93), boroughs (1.70: 1.40, 2.07), and cities (3.71: 1.74, 7.92). Similarly, corresponding odds ratios (95% CI) for forest edge density were 1.34 (1.22, 1.47), 1.56 (1.33, 1.82), and 1.90 (1.13, 3.18). Associations were generally higher in residential buffers, compared to community, and in cities, compared to boroughs or townships. Our results reinforce the importance of peridomestic landscape in Lyme disease risk, particularly measures that reflect human interaction with tick habitat. Linkage of EHR data to public data on residential and community context may lead to new health system-based approaches for improving Lyme disease diagnosis, treatment, and prevention.
土地利用和森林破碎化被认为是莱姆病发病率及其地理分布的主要驱动因素。我们在宾夕法尼亚州盖辛格卫生系统进行了一项基于人群的病例对照研究，以检验景观组成和配置与莱姆病之间的关联。莱姆病病例（n=9657）通过电子健康记录（EHR）中的诊断代码、实验室代码和抗生素订单的组合来识别。对照（5:1）按年份、年龄和性别随机选择并频率匹配。我们根据先前的文献，基于国家土地覆盖数据库和 MODIS 卫星图像测量了六个景观变量：绿色度（归一化植被指数）、森林百分比、草本百分比、森林边缘密度、森林-草本边缘百分比和平均森林斑块大小。我们在社区和半英里[805 m]欧几里得住宅缓冲区两个空间范围内分配景观变量。在按社区类型分层的模型中，将景观变量建模为三分位数和灵活样条，并根据人口统计学和临床协变量调整关联。一般来说，我们观察到景观指标与莱姆病之间存在正相关关系，除了草本百分比，其关联因社区类型而异。例如，与最低三分位数相比，住宅缓冲区中绿色度最高三分位数的个体患莱姆病的几率更高（比值比：95%置信区间[CI]）在镇（1.73：1.55，1.93）、自治市（1.70：1.40，2.07）和城市（3.71：1.74，7.92）。同样，森林边缘密度的相应比值比（95%CI）分别为 1.34（1.22，1.47）、1.56（1.33，1.82）和 1.90（1.13，3.18）。与社区相比，住宅缓冲区的关联通常更高，与自治市相比，城市的关联更高。与自治市或镇相比。我们的结果强调了围内景观在莱姆病风险中的重要性，特别是反映人类与蜱栖息地相互作用的措施。EHR 数据与住宅和社区环境的公共数据的链接可能会导致新的基于卫生系统的方法，以改善莱姆病的诊断、治疗和预防。
