乳腺癌死亡率是一个复杂的问题，传统的方法假设决定死亡率的因素在地理空间和尺度上对死亡率的影响是稳定的。为了确定人口统计学、环境、生活方式和医疗保健接触与美国县级乳腺癌死亡率之间的地理变异，本地理空间横断面研究利用了来自美国监测、流行病学和结果 (SEER) 数据库的成年女性乳腺癌患者数据。使用2015年至2019年美国2176个县的调整死亡率进行统计和空间分析。数据分析时间为2022年7月。县级人口统计学、环境、生活方式和医疗保健接触变量来自开放数据源。模型系数用于描述18个变量与年龄调整后的乳腺癌死亡率之间的关联。与多元线性回归 (OLS) 相比，多尺度地理加权回归 (MGWR) 松弛了空间站点性的假设，并允许系数的幅度、方向和显著性在地理空间上发生变化。OLS和MGWR模型均一致认为县级年龄调整后的乳腺癌死亡率与肥胖显著正相关（OLS：β，1.21；95% CI，0.88至1.54；平均 [SD] MGWR：β，0.72 [0.02]），与通过乳腺X线检查筛查的成年人比例显著负相关（OLS：β，-1.27；95% CI，-1.70至-0.84；平均 [SD] MGWR：β，-1.07 [0.16]）。此外，MGWR模型揭示了这两个决定因素在整个美国对死亡率的影响是稳定的。然而，MGWR模型为不同地区的县级其他因素与乳腺癌死亡率的关联提供了重要见解。两个模型均认为吸烟（OLS：β，-0.65；95% CI，-0.98至-0.32；平均 [SD] MGWR：β，-0.75 [0.92]）、食品环境指数（OLS：β，-1.35；95% CI，-1.72至-0.98；平均 [SD] MGWR：β，-1.69 [0.70]）、运动机会（OLS：β，-0.56；95% CI，-0.91至-0.21；平均 [SD] MGWR：β，-0.59 [0.81]）、种族隔离（OLS：β，-0.60；95% CI，-0.89至-0.31；平均 [SD] MGWR：β，-0.47 [0.41]）、心理健康医生比例（OLS：β，-0.93；95% CI，-1.44至-0.42；平均 [SD] MGWR：β，-0.48 [0.92]）和基本医疗医生比例（OLS：β，-1.46；95% CI，-2.13至-0.79；平均 [SD] MGWR：β，-1.06 [0.57]）与乳腺癌死亡率呈负相关，而光污染则呈正相关（OLS：β，0.48；95% CI，0.24至0.72；平均 [SD] MGWR：β，0.27 [0.04]）。但在MGWR模型中，效果大小和显著性在地理区域上存在变异。相反，OLS模型发现残疾对乳腺癌死亡率不是一个显著的变量，然而MGWR模型发现在某些地理位置上它与死亡率呈显著正相关。这个横断面研究发现，并非所有与乳腺癌死亡率相关的社会决定因素在空间上是稳定的，并为公共卫生从业者提供了空间显性见解，以指导地理上针对性的干预措施。

Breast cancer mortality is complex and traditional approaches that seek to identify determinants of mortality assume that their effects on mortality are stationary across geographic space and scales.To identify geographic variation in the associations of population demographics, environmental, lifestyle, and health care access with breast cancer mortality at the US county-level.This geospatial cross-sectional study used data from the Surveillance, Epidemiology, and End Results (SEER) database on adult female patients with breast cancer. Statistical and spatial analysis was completed using adjusted mortality rates from 2015 to 2019 for 2176 counties in the US. Data were analyzed July 2022.County-level population demographics, environmental, lifestyle, and health care access variables were obtained from open data sources.Model coefficients describing the association between 18 variables and age-adjusted breast cancer mortality rate. Compared with a multivariable linear regression (OLS), multiscale geographically weighted regression (MGWR) relaxed the assumption of spatial stationarity and allowed for the magnitude, direction, and significance of coefficients to change across geographic space.Both OLS and MGWR models agreed that county-level age-adjusted breast cancer mortality rates were significantly positively associated with obesity (OLS: β, 1.21; 95% CI, 0.88 to 1.54; mean [SD] MGWR: β, 0.72 [0.02]) and negatively associated with proportion of adults screened via mammograms (OLS: β, -1.27; 95% CI, -1.70 to -0.84; mean [SD] MGWR: β, -1.07 [0.16]). Furthermore, the MGWR model revealed that these 2 determinants were associated with a stationary effect on mortality across the US. However, the MGWR model provided important insights on other county-level factors differentially associated with breast cancer mortality across the US. Both models agreed that smoking (OLS: β, -0.65; 95% CI, -0.98 to -0.32; mean [SD] MGWR: β, -0.75 [0.92]), food environment index (OLS: β, -1.35; 95% CI, -1.72 to -0.98; mean [SD] MGWR: β, -1.69 [0.70]), exercise opportunities (OLS: β, -0.56; 95% CI, -0.91 to -0.21; mean [SD] MGWR: β, -0.59 [0.81]), racial segregation (OLS: β, -0.60; 95% CI, -0.89 to -0.31; mean [SD] MGWR: β, -0.47 [0.41]), mental health care physician ratio (OLS: β, -0.93; 95% CI, -1.44 to -0.42; mean [SD] MGWR: β, -0.48 [0.92]), and primary care physician ratio (OLS: β, -1.46; 95% CI, -2.13 to -0.79; mean [SD] MGWR: β, -1.06 [0.57]) were negatively associated with breast cancer mortality, and that light pollution was positively associated (OLS: β, 0.48; 95% CI, 0.24 to 0.72; mean [SD] MGWR: β, 0.27 [0.04]). But in the MGWR model, the magnitude of effect sizes and significance varied across geographical regions. Inversely, the OLS model found that disability was not a significant variable for breast cancer mortality, yet the MGWR model found that it was significantly positively associated in some geographical locations.This cross-sectional study found that not all social determinants associated with breast cancer mortality are spatially stationary and provides spatially explicit insights for public health practitioners to guide geographically targeted interventions.