Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina.
Duke Cancer Institute, Duke University School of Medicine, Durham, North Carolina.
Cancer Epidemiol Biomarkers Prev. 2023 Jan 9;32(1):74-81. doi: 10.1158/1055-9965.EPI-22-0650.
Ovarian cancer survival disparities have persisted for decades, driven by lack of access to quality treatment. We conducted structural equation modeling (SEM) to define latent variables representing three healthcare access (HCA) domains: affordability, availability, and accessibility, and evaluated the direct and indirect associations between race and ovarian cancer treatment mediated through the HCA domains.
Patients with ovarian cancer ages 65 years or older diagnosed between 2008 and 2015 were identified from the SEER-Medicare dataset. Generalized SEM was used to estimate latent variables representing HCA domains by race in relation to two measures of ovarian cancer-treatment quality: gynecologic oncology consultation and receipt of any ovarian cancer surgery.
A total of 8,987 patients with ovarian cancer were included in the analysis; 7% were Black. The affordability [Ω: 0.876; average variance extracted (AVE) = 0.689], availability (Ω: 0.848; AVE = 0.636), and accessibility (Ω: 0.798; AVE = 0.634) latent variables showed high composite reliability in SEM analysis. Black patients had lower affordability and availability, but higher accessibility compared with non-Black patients. In fully adjusted models, there was no direct effect observed between Black race to receipt of surgery [β: -0.044; 95% confidence interval (CI), -0.264 to 0.149]; however, there was an inverse total effect (β: -0.243; 95% CI, -0.079 to -0.011) that was driven by HCA affordability (β: -0.025; 95% CI, -0.036 to -0.013), as well as pathways that included availability and consultation with a gynecologist oncologist.
Racial differences in ovarian cancer treatment appear to be driven by latent variables representing healthcare affordability, availability, and accessibility.
Strategies to mitigate disparities in multiple HCA domains will be transformative in advancing equity in cancer treatment.
卵巢癌的生存差距持续了几十年，这是由于缺乏获得高质量治疗的机会造成的。我们进行了结构方程建模（SEM），定义了代表三个医疗保健可及性（HCA）领域的潜在变量：负担能力、可及性和可及性，并评估了种族与卵巢癌治疗之间通过 HCA 领域介导的直接和间接关联。
从 SEER-Medicare 数据集确定了 2008 年至 2015 年间诊断为 65 岁或以上的卵巢癌患者。广义 SEM 用于根据种族估计代表 HCA 领域的潜在变量，以两种卵巢癌治疗质量衡量标准：妇科肿瘤学咨询和接受任何卵巢癌手术。
共纳入 8987 例卵巢癌患者进行分析；7%为黑人。负担能力[Ω：0.876；平均方差提取（AVE）= 0.689]、可用性（Ω：0.848；AVE = 0.636）和可及性（Ω：0.798；AVE = 0.634）的潜在变量在 SEM 分析中显示出较高的综合可靠性。与非黑人患者相比，黑人患者的负担能力和可用性较低，但可及性较高。在完全调整的模型中，黑种人种族与手术接受之间没有观察到直接影响[β：-0.044；95%置信区间（CI），-0.264 至 0.149]；然而，存在一个反向总效应（β：-0.243；95%CI，-0.079 至 -0.011），这是由 HCA 的负担能力（β：-0.025；95%CI，-0.036 至 -0.013）以及包括可用性和妇科肿瘤学家咨询在内的途径驱动的。
卵巢癌治疗中的种族差异似乎是由代表医疗保健负担能力、可及性和可及性的潜在变量驱动的。
减轻多个 HCA 领域差异的策略将在推进癌症治疗公平方面具有变革性。
