Meyer Shelby, Woldu Henok G, Sheets Lincoln R
University of Missouri School of Medicine, Columbia, MO, USA.
Department of Health Management and Informatics, University of Missouri, Columbia, MO, USA.
Contemp Clin Trials Commun. 2021 Jan 20;21:100718. doi: 10.1016/j.conctc.2021.100718. eCollection 2021 Mar.
Cancer clinical trials (CCT) offer significant potential benefit, not only for future patients but also for enrolled participants, yet a very small minority of cancer patients participate, resulting in low levels of enrollment that have stalled clinical trials dramatically. Though many have endeavored to study this phenomenon, relatively little research has explored the demographic factors which may affect CCT enrollment. Understanding patient demographics is critical to optimizing enrollment, evaluating generalizability, and ensuring equity of CCT.
To better understand the effect of social determinants of health on CCT enrollment, the authors constructed a multivariable logistic regression model to analyze data collected in the last ten years in the CDC Behavioral Risk Factor Surveillance System (BRFSS) Survey, an annual national survey conducted among the non-institutionalized adult population of the U.S.
In multivariable regression analysis, enrollment varied significantly with sociodemographic factors. Individuals of higher income, Hispanic ethnicity, and younger age were most likely to participate in CCTs. Enrollment did not vary significantly by educational attainment.
Our multivariable analysis indicated people of color are more likely to participate in CCT, perhaps demonstrating that structural barriers shape participation more than race alone. Efforts to improve CCT enrollment may benefit from a shift in focus towards access to care by alleviating structural and financial barriers to enrollment.
癌症临床试验(CCT)不仅对未来患者,而且对入组参与者都具有显著的潜在益处,但只有极少数癌症患者参与其中,导致入组率极低,严重阻碍了临床试验的进展。尽管许多人致力于研究这一现象,但相对较少的研究探讨了可能影响CCT入组的人口统计学因素。了解患者人口统计学对于优化入组、评估普遍性以及确保CCT的公平性至关重要。
为了更好地理解健康的社会决定因素对CCT入组的影响,作者构建了一个多变量逻辑回归模型,以分析过去十年在美国疾病控制与预防中心行为风险因素监测系统(BRFSS)调查中收集的数据,该调查是对美国非机构化成年人口进行的年度全国性调查。
在多变量回归分析中,入组情况因社会人口统计学因素而有显著差异。高收入、西班牙裔和较年轻的个体最有可能参与CCT。入组情况在受教育程度方面没有显著差异。
我们的多变量分析表明,有色人种更有可能参与CCT,这或许表明结构性障碍对参与的影响大于种族本身。改善CCT入组的努力可能受益于将重点转向通过减轻入组的结构性和经济障碍来获得医疗服务。