美国癌症死亡率差异的人口模拟模型。

Jeanne S Mandelblatt, Clyde B Schechter, Natasha K Stout, Hui Huang, Sarah Stein, Christina Hunter Chapman, Amy Trentham-Dietz, Jinani Jayasekera, Ronald E Gangnon, John M Hampton, Linn Abraham, Ellen S O'Meara, Vanessa B Sheppard, Sandra J Lee
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引用次数: 0

摘要

背景:非裔美国人或黑人女性的乳腺癌症死亡率持续高于美国总人口,尽管经年龄调整的发病率略低。方法:三个癌症干预和监测建模网络模拟小组模拟了黑人女性人口与美国总体人口之间的癌症死亡率差异。模型输入使用了来自临床试验、国家登记、全国代表性调查和观察性研究的特定种族群体数据。分析从总体人群中癌症死亡率开始,依次替换黑人人群的参数,以量化由于人口统计学、发病率、获得筛查和治疗的机会以及肿瘤生物学和治疗反应的差异而导致的癌症道德差异模型的百分比。结果:3个模型的结果相似。2019年,发病率和竞争死亡率的种族差异占死亡率差异的净1%,而肿瘤亚型和分期分布平均占20%(各模型范围 = 13%-24%),筛查平均占3%(范围 = 3%-4%)。治疗参数占建模死亡率差异的大部分:平均值 = 17%(范围 = 16%-19%)用于治疗开始和平均值 = 61%(范围 = 57%-63%)的真实世界有效性。结论:我们的模型结果表明,以改善治疗机会为目标的政策变化可以增加癌症的公平性。研究结果还强调,努力必须超越针对治疗开始公平的政策,包括高质量的治疗完成。这项研究将有助于未来的建模,以测试不同具体政策变化对死亡率差异的影响。
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Population simulation modeling of disparities in US breast cancer mortality.

Background: Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence.

Methods: Three Cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group-specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy.

Results: Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness.

Conclusion: Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities.

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