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 risk, but not on spatial variations in associations 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.

中文翻译：

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探索马里兰州巴尔的摩市2002-2005年淋病发病率的空间变化人口统计学关联

在淋病风险和人口统计学特征之间建立空间联系的能力是疾病意识和更有效的预防技术的重要一步。过去的空间分析关注风险的局部变化，而不关注关联的空间变化与受众特征。我们从2002年至2005年从巴尔的摩市卫生局收集数据，并通过使用Poisson地理加权回归（PGWR）允许协会中的空间变异，评估了已知与巴尔的摩淋病风险相关的人口统计学特征。PGWR地图揭示了种族，教育程度和有淋病风险的贫困之间的局部关系变化，这是以前没有捕获到的。我们确定PGWR模型可以更好地拟合数据，并对风险的“核心领域”产生更细微的解释。PGWR模型对疾病风险与人口统计学特征之间的关联的空间变化进行量化，为较高风险的核心区域提供了局部和人口统计学结构。