Switchenko Jeffrey M, Jennings Jacky M, Waller Lance A
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA.
Department of Pediatrics, Center for Child and Community Health Resources, Johns Hopkins University, Baltimore, MD, USA.
J Geogr Syst. 2020 Apr;22(2):201-216. doi: 10.1007/s10109-020-00321-7. Epub 2020 Feb 21.
The ability to establish spatial links between gonorrhea risk and demographic features is an important step in disease awareness and more effective prevention techniques. Past spatial analyses focused on local variations in , but not on spatial variations in with demographics. We collected data from the Baltimore City Health Department from 2002 to 2005 and evaluated demographic features known to be associated with gonorrhea risk in Baltimore, by allowing spatial variation in associations using Poisson geographically weighted regression (PGWR). The PGWR maps revealed variations in local relationships between race, education, and poverty with gonorrhea risk which were not captured previously. We determined that the PGWR model provided a significantly better fit to the data and yields a more nuanced interpretation of "core areas" of risk. The PGWR model's quantification of spatial variation in associations between disease risk and demographic features provides local and demographic structure to core areas of higher risk.
在淋病风险与人口统计学特征之间建立空间联系的能力,是提高疾病认知和采用更有效预防技术的重要一步。过去的空间分析侧重于[此处原文缺失部分内容]的局部差异,而非[此处原文缺失部分内容]与人口统计学的空间差异。我们收集了巴尔的摩市卫生部门2002年至2005年的数据,并通过使用泊松地理加权回归(PGWR)允许关联存在空间变化,评估了巴尔的摩已知与淋病风险相关的人口统计学特征。PGWR地图揭示了种族、教育程度和贫困与淋病风险之间的局部关系变化,这些变化以前未被发现。我们确定PGWR模型对数据的拟合效果明显更好,并且对风险“核心区域”能产生更细致入微的解释。PGWR模型对疾病风险与人口统计学特征之间关联的空间变化进行量化,为高风险核心区域提供了局部和人口统计学结构。