我们正在针对 4 种恶性肿瘤（多发性骨髓瘤、膀胱癌、胃癌和子宫癌）开发 10 个从头群体水平的数学模型。这些站点中的每一个都记录了结果的差异，这些差异被认为是系统性种族主义的下游影响。作为癌症干预和监测建模网络孵化器计划的一部分，正在独立开发十个模型。这些模型模拟一般人群癌症发病率、早期诊断、治疗和死亡率的趋势，并按种族亚组进行分层。模型输入基于大量人口数据集、临床试验和观察研究。一些核心参数是共享的，其他参数是特定于模型的。所有模型都是微观模拟模型，使用自我报告的竞赛对模型输入进行分层。它们可以模拟美国出生队列和人群中相关风险因素（例如吸烟、肥胖）和保险状况（多发性骨髓瘤和子宫癌）的分布。这些模型旨在完善 4 种癌症的预防、检测和管理方法考虑到不确定性和限制。他们将帮助探索观察到的种族差异是否可以用不平等来解释，评估现有和潜在的癌症预防和控制政策对健康公平和差异的影响，并确定在降低癌症死亡率方面平衡效率和公平的政策。©作者）2023 年。由牛津大学出版社出版。版权所有。如需权限，请发送电子邮件至：journals.permissions@oup.com。

We are developing 10 de novo population-level mathematical models in 4 malignancies (multiple myeloma and bladder, gastric, and uterine cancers). Each of these sites has documented disparities in outcome that are believed to be downstream effects of systemic racism.Ten models are being independently developed as part of the Cancer Intervention and Surveillance Modeling Network incubator program. These models simulate trends in cancer incidence, early diagnosis, treatment, and mortality for the general population and are stratified by racial subgroup. Model inputs are based on large population datasets, clinical trials, and observational studies. Some core parameters are shared, and other parameters are model specific. All models are microsimulation models that use self-reported race to stratify model inputs. They can simulate the distribution of relevant risk factors (eg, smoking, obesity) and insurance status (for multiple myeloma and uterine cancer) in US birth cohorts and population.The models aim to refine approaches in prevention, detection, and management of 4 cancers given uncertainties and constraints. They will help explore whether the observed racial disparities are explainable by inequities, assess the effects of existing and potential cancer prevention and control policies on health equity and disparities, and identify policies that balance efficiency and fairness in decreasing cancer mortality.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.