A Role of Multilevel Healthcare Access Dimensions in Ovarian Cancer Disparities

在美国，不到40%的卵巢癌（OC）患者接受了符合分期指南的治疗。 手术和化疗;黑OC患者更不可能接受这种治疗。虽然5年 1975年至2009年，白色OC患者的相对生存率提高了47%， 在同一时期的黑人患者。在癌症幸存者中，也观察到黑人患者 与白色幸存者相比，抑郁、疼痛和疲劳显著更高。这些种族差异可能是由于 这主要是由于医疗保健获得方面的差异-特别是获得高质量的初始治疗和治疗后的差异- 治疗支持护理。获得医疗保健是一个复杂的问题;然而，Penchansky医疗保健 获得框架建议，它包括五个具体方面：可获得性，可负担性， 保健服务的可获得性、便利性和可接受性。我们的研究将全面 评估黑人和白色患者的医疗保健获取（HCA）的所有五个维度，以识别和 量化导致OC护理和生存率显着种族差异的具体因素。更 具体而言，我们将利用来自SEER医疗保险（8，060名OC患者）的数据以及主要调查数据沿着 来自1,010名OC患者的基于人群的样本，与几个现有的数据集（例如，美国 社区调查，地区医疗保健资源文件），以描述相关性中的种族差异 每个HCA维度与三个结果之间的关系：初始治疗和支持性护理的质量， 根据患者报告的流行但可管理的症状和生存率的结果确定寿命。我们将 评估HCA在患者、社区、提供者和医院层面的维度，并利用分层 用随机效应回归模型来解释聚类，用多级结构方程模型来解释聚类。 估计种族对通过HCA维度介导的治疗的总的、直接的和间接的影响。我们 初步研究表明某些研究不足的维度（例如，可接受性）可能超过其他 尺寸（例如，在确定护理质量方面。此外，各种HCA的影响 尺寸可以根据种族而变化。通过分析黑人和白色患者的高质量多级数据集， 我们可以充分描述种族差异的性质，评估种族差异的相对重要性， 障碍，并确定种族特定的可改变的因素。我们的研究将提供新颖的，经验的， 关于HCA维度对OC结果的独特和集体影响的可概括的见解。 这些见解将有助于确定和优先考虑特定的可修改因素，然后可以针对性地减少 并改善对所有患者的护理。

Less than 40% of ovarian cancer (OC) patients in the US receive stage-appropriate guideline- adherent surgery and chemotherapy; Black OC patients are even less likely to receive such treatment. While 5-year relative survival for White OC patients improved by 47% between 1975 and 2009, it declined by 27% for Black patients during this same period. Among cancer survivors, Black patients are also observed to have significantly higher depression, pain, and fatigue than White survivors. These racial disparities are likely due largely to differences in healthcare access – specifically, access to high quality initial treatment and post- treatment supportive care. Healthcare access is a complex subject; however, the Penchansky healthcare access framework proposed that it comprises of five specific dimensions: availability, affordability, accessibility, accommodation and acceptability of health care services. Our study will comprehensively evaluate all five dimensions of healthcare access (HCA) among Black and White patients to identify and quantify the specific factors contributing to the striking racial differences in OC care and survival. More specifically, we will utilize data from SEER-Medicare (8,060 OC patients) along with primary survey data from a population-based sample of 1,010 OC patients, linked with several existing datasets (e.g., American Community Survey, Area Healthcare Resource File), to characterize racial differences in associations between each HCA dimension and three outcomes: quality of initial treatment and supportive care, quality of life based on patient-reported outcomes in prevalent yet manageable symptoms, and survival. We will evaluate HCA dimensions across patient, neighborhood, provider and hospital levels, and utilize hierarchical regression models with random effects to account for clustering, and multilevel structural equation models to estimate the total, direct and indirect effect of race on treatment mediated through HCA dimensions. Our preliminary studies suggest that certain under-studied dimensions (e.g., acceptability) may outweigh other dimensions (e.g., availability) in determining quality of care. Moreover, the impact of the various HCA dimensions may vary by race. By analyzing high-quality multilevel datasets with Black and White patients, we can fully characterize the nature of racial disparities, assess the relative importance of race-specific barriers to care, and identify race-specific modifiable factors. Our study will provide novel, empirical, and generalizable insights regarding the distinct and collective influence of HCA dimensions on OC outcomes. These insights will help identify and prioritize specific modifiable factors that can then be targeted to reduce disparities and improve care for all patients.