开发新癌症人群模型的方法,以检查膀胱、胃和子宫内膜癌症和多发性骨髓瘤中的癌症和种族问题:癌症干预和监测建模网络孵化器计划。

Yuliia Sereda, Fernando Alarid-Escudero, Nina A Bickell, Su-Hsin Chang, Graham A Colditz, Chin Hur, Hawre Jalal, Evan R Myers, Tracy M Layne, Shi-Yi Wang, Jennifer M Yeh, Thomas A Trikalinos
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引用次数: 0

摘要

背景:我们正在开发4种恶性肿瘤(多发性骨髓瘤和膀胱癌、胃癌和子宫癌)的10个新的群体水平数学模型。每个网站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。方法:10个模型正在独立开发,作为癌症干预和监测建模网络孵化器计划的一部分。这些模型模拟了普通人群癌症发病率、早期诊断、治疗和死亡率的趋势,并按种族分组进行了分层。模型输入基于大型人群数据集、临床试验和观察性研究。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,使用自我报告的种族来对模型输入进行分层。他们可以模拟相关风险因素(如吸烟、肥胖)和保险状况(多发性骨髓瘤和子宫癌症)在美国出生队列和人群中的分布。讨论:在不确定性和限制条件下,这些模型旨在完善4种癌症的预防、检测和管理方法。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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approaches to developing de novo cancer population models to examine questions about cancer and race in bladder, gastric, and endometrial cancer and multiple myeloma: the Cancer Intervention and Surveillance Modeling Network incubator program.

Background: 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.

Methods: 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.

Discussion: 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.

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