Many studies have examined social determinants of health (SDoH) factors independently, overlooking their interconnected and intersectional nature. Our study takes a multifactorial approach to construct a neighborhood level measure of SDoH and explores how neighborhood residency impacts care received by endometrial cancer patients in Massachusetts. We used a Bayesian multivariate Bernoulli mixture model to create and characterize neighborhood SDoH (NSDoH) profiles using the 2015-2019 American Community Survey at the census tract level (n=1478), incorporating 18 variables across four domains: housing conditions and resources, economic security, educational attainment, and social and community context. We linked these profiles to Massachusetts Cancer Registry data to estimate the odds of receiving optimal care for endometrial cancer using Bayesian multivariate logistic regression. The model identified eight NSDoH profiles. Profiles 1 and 2 accounted for 27% and 25% of census tracts, respectively. Profile 1 featured neighborhoods with high homeownership, above median incomes, and high education, while Profile 2 showed higher probabilities of limited English proficiency, renters, lower education, and working class jobs. After adjusting for sociodemographic and clinical characteristics, we found no statistically significant association between NSDoH profiles and receipt of optimal care. However, compared to patients in NSDoH Profile 1, those in Profile 2 had lower odds of receiving optimal care, OR = 0.77, 95% CI (0.56, 1.07). Our results demonstrate the interconnected and multidimensional nature of NSDoH, underscoring the importance of modeling them accordingly. This study also highlights the need for targeted interventions at the neighborhood level to address underlying drivers of health disparities, ensure equitable healthcare delivery, and foster better outcomes for all patients.

翻译：现有研究多独立考察社会健康决定因素，忽视了其相互关联与交叉影响的本质。本研究采用多因素分析方法构建邻里层面的社会健康决定因素度量指标，并探究马萨诸塞州子宫内膜癌患者所在社区如何影响其接受的医疗照护。我们基于2015-2019年美国社区调查数据（普查区层面，n=1478），采用贝叶斯多元伯努利混合模型构建并刻画邻里社会健康决定因素特征谱，涵盖住房条件与资源、经济保障、教育程度、社会与社区环境四大领域的18个变量。通过将这些特征谱与马萨诸塞州癌症登记数据关联，我们运用贝叶斯多元逻辑回归估算了子宫内膜癌患者接受优化治疗的几率。模型识别出八种邻里社会健康决定因素特征谱，其中特征谱1和2分别涵盖27%和25%的普查区。特征谱1对应高住房自有率、收入中位数以上及高教育水平的社区，而特征谱2则表现为英语水平有限、租赁住房比例较高、教育程度较低及从事工人阶级职业的概率更高。在调整社会人口学与临床特征后，我们发现邻里社会健康决定因素特征谱与接受优化治疗之间无统计学显著关联。然而，与特征谱1的患者相比，特征谱2患者接受优化治疗的几率较低（OR=0.77，95% CI [0.56, 1.07]）。研究结果揭示了邻里社会健康决定因素相互关联的多维本质，强调了建立相应模型的重要性。本研究同时指出，需要在社区层面实施针对性干预措施，以消除健康差异的内在驱动因素，确保医疗服务的公平可及，并为所有患者创造更佳的健康结局。