Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milano, Italy.
Division of Early Drug Development, IEO European Institute of Oncology, IRCCS, Milano, Italy.
ESMO Open. 2022 Jun;7(3):100515. doi: 10.1016/j.esmoop.2022.100515. Epub 2022 Jun 21.
Clinical trials are increasingly perceived as a therapeutic opportunity for cancer patients. Favoring their concentration in few high-expertise academic centers maximizes quality of data collection but poses an issue of access equality. Analytical tools to quantify trial accessibility are needed to rationalize resources.
We constructed a distance-based accessibility index (dAI) using publicly available data on demographics, cancer incidence and trials. Multiple strategies were applied to mitigate or quantify clear sources of bias: reporting biases by text mining multiple registries; reliability of simple geographical distance by comparison with high-quality travel cost data for Italy; index inflation due to highly heterogeneous cancer incidence by log-transformation. We studied inequalities by Gini index and time trend significance by Mann-Kendall test. We simulated different resource allocation models in representative countries and identified locations where new studies would maximally improve the national index.
The dAI approximated well a more realistic but not widely applicable travel cost-based index. Accessibility was unevenly distributed across and within countries (Gini index ∼0.75), with maximal inequalities in high- and upper-middle-income countries (China, United States, Russian Federation). Over time, accessibility increased but less than the total number of trials, most evidently in upper-middle-income countries. Simulations in representative countries (Italy and Serbia) identified ideal locations able to maximally raise the national index.
Access to clinical trials is highly uneven across and within countries and is not mitigated by simple increase in the number of trials; a rational algorithmic approach can be used to mitigate inequalities.
临床试验越来越被视为癌症患者的一种治疗机会。将其集中在少数高专业学术中心，可最大限度地提高数据收集质量，但会带来公平性问题。需要分析工具来量化试验的可及性，以合理配置资源。
我们使用人口统计学、癌症发病率和试验方面的公开数据构建了一个基于距离的可及性指数（dAI）。应用了多种策略来减轻或量化明显的偏差来源：通过挖掘多个注册中心的文本来减轻报告偏差；通过与意大利高质量旅行成本数据进行比较来减轻简单地理距离的可靠性偏差；通过对数转换来减轻由于癌症发病率高度异质导致的指数膨胀。我们通过基尼指数研究了不平等现象，通过曼肯德尔检验研究了时间趋势的显著性。我们在代表性国家模拟了不同的资源分配模型，并确定了在哪里开展新的研究可以最大限度地提高国家指数。
dAI 很好地逼近了一个更现实但不广泛适用的基于旅行成本的指数。可及性在国家内部和国家之间分布不均（基尼指数约为 0.75），高收入和中上收入国家（中国、美国、俄罗斯联邦）的不平等程度最大。随着时间的推移，可及性有所增加，但不及试验总数的增加，中上收入国家的增加最为明显。在代表性国家（意大利和塞尔维亚）的模拟中，确定了能够最大限度地提高国家指数的理想地点。
临床试验的可及性在国家内部和国家之间极不均衡，增加试验数量并不能减轻这种不均衡；可以使用合理的算法方法来减轻不平等。
