Department of Geography and Urban Studies, Temple University, Philadelphia, Pennsylvania.
New Jersey State Cancer Registry, Cancer Epidemiology Services, New Jersey Department of Health, Trenton, New Jersey.
Cancer Epidemiol Biomarkers Prev. 2019 Dec;28(12):1958-1967. doi: 10.1158/1055-9965.EPI-19-0416. Epub 2019 Oct 24.
Mapping breast cancer survival can help cancer control programs prioritize efforts with limited resources. We used Bayesian spatial models to identify whether breast cancer survival among patients in New Jersey (NJ) varies spatially after adjusting for key individual (age, stage at diagnosis, molecular subtype, race/ethnicity, marital status, and insurance) and neighborhood measures of poverty and economic inequality [index of concentration at the extremes (ICE)].
Survival time was calculated for all NJ women diagnosed with invasive breast cancer between 2010 and 2014 and followed to December 31, 2015 ( = 27,078). Nonlinear geoadditive Bayesian models were used to estimate spatial variation in hazard rates and identify geographic areas of higher risk of death from breast cancer.
Significant geographic differences in breast cancer survival were found in NJ. The geographic variation of hazard rates statewide ranged from 0.71 to 1.42 after adjustment for age and stage, and were attenuated after adjustment for additional individual-level factors (0.87-1.15) and neighborhood measures, including poverty (0.9-1.11) and ICE (0.92-1.09). Neighborhood measures were independently associated with breast cancer survival, but we detected slightly stronger associations between breast cancer survival, and the ICE compared to poverty.
The spatial models indicated breast cancer survival disparities are a result of combined individual-level and neighborhood socioeconomic factors. More research is needed to understand the moderating pathways in which neighborhood socioeconomic status influences breast cancer survival.
More effective health interventions aimed at improving breast cancer survival could be developed if geographic variation were examined more routinely in the context of neighborhood socioeconomic inequalities in addition to individual characteristics.
绘制乳腺癌生存曲线有助于癌症控制项目在资源有限的情况下有针对性地开展工作。我们使用贝叶斯空间模型，在调整了关键的个体因素（年龄、诊断时的分期、分子亚型、种族/民族、婚姻状况和保险状况）和衡量贫困和经济不平等的邻里指标（极值集中指数 (ICE)）后，确定新泽西州 (NJ) 乳腺癌患者的生存情况是否存在空间差异。
我们计算了 2010 年至 2014 年间在 NJ 被诊断患有浸润性乳腺癌并随访至 2015 年 12 月 31 日的所有女性患者的生存时间（=27078 人）。使用非线性地理加性贝叶斯模型来估计危险率的空间变化，并确定乳腺癌死亡风险较高的地理区域。
在 NJ 发现乳腺癌生存存在显著的地理差异。在调整年龄和分期后，全州范围内危险率的地理变化范围为 0.71 至 1.42，在调整了更多的个体因素（0.87-1.15）和邻里指标（包括贫困和 ICE）后，危险率的变化范围缩小到 0.9-1.11 和 0.92-1.09。邻里指标与乳腺癌生存独立相关，但我们发现乳腺癌生存与 ICE 之间的关联略强于与贫困之间的关联。
空间模型表明，乳腺癌生存的差异是个体水平和邻里社会经济因素共同作用的结果。需要进一步研究，以了解邻里社会经济地位影响乳腺癌生存的调节途径。
如果在考虑个体特征的基础上，更常规地在邻里社会经济不平等的背景下检查地理差异，可能会开发出更有效的旨在提高乳腺癌生存的健康干预措施。
