Comparative modeling of gastric cancer disparities and prevention in the US and globally
美国和全球胃癌差异和预防的比较模型
基本信息
- 批准号:10330855
- 负责人:
- 金额:$ 91.92万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-15 至 2026-08-31
- 项目状态:未结题
- 来源:
- 关键词:AccountingAddressAdultAgeAntibiotic ResistanceAsiansCancer BurdenCancer ControlCancer EtiologyCancer ModelCaringCessation of lifeClinicalCountryDataDatabasesDiagnosisEthnic OriginExcisionFutureGastric AdenocarcinomaGoalsHelicobacter InfectionsHelicobacter pyloriHispanicsIncidenceIndividualInfectionInterventionLesionMalignant NeoplasmsMethodologyModelingNeoplasmsOperative Surgical ProceduresOutcomePatientsPersonsPoliciesPopulationPositioning AttributePrevalencePreventionPrevention strategyPrimary Health CarePrimary PreventionProspective StudiesPublicationsRaceRandomized Controlled TrialsRegimenResearchRiskRisk FactorsScreening for Gastric CancerSecond Primary CancersSecondary PreventionSmokingStomachStudy modelsSubgroupSurvival RateUrsidae FamilyVariantVulnerable PopulationsWorkWorld Health Organizationburden of illnesscancer carecancer health disparitycancer preventioncomparativecost effectivecost effectivenessdisease natural historyearly onsetexperiencegastric cancer preventiongastric intestinal metaplasiahigh riskhigh risk populationimprovedinnovationmalignant stomach neoplasmminority communitiesmodel developmentmodels and simulationmortalitymortality disparityneglectpopulation healthpremalignantscreeningsexsimulationsmoking cessationsurveillance strategytooltransmission processtrendyoung woman
项目摘要
Gastric cancer (GC), specifically gastric adenocarcinoma, is the fifth most common cancer and the third
leading cause of cancer death globally and has been categorized as a neglected cancer by the World Health
Organization. In the U.S., there are stark disparities, with Blacks, Hispanics and Asians having a nearly two-
fold greater risk of developing or dying from GC compared to Whites, reflecting differences in risk factors, such
as Helicobacter pylori (H. pylori) infection and smoking, as well as access to primary prevention and care.
Several factors are changing the landscape of GC prevention, including a better understanding of the disease
natural history, new evidence on prevention from prospective studies, and anticipated results from randomized
controlled trials. As early GC detection can improve survival by allowing for curative surgical or noninvasive
endoscopic resection, new targeted approaches to GC prevention have the potential to markedly improve
population health and reduce GC disparities within the U.S. Although H. pylori has been the primary focus of
global GC prevention efforts to date, substantial variation by subpopulation in H. pylori prevalence in the U.S.
and the world has accentuated the need to optimize H. pylori screen-and-treat interventions for vulnerable
groups. One approach is targeted endoscopic screening of high-risk individuals. For instance, persons found to
have gastric intestinal metaplasia, a precursor lesion associated with a high progression risk to gastric
neoplasia, are recommended to undergo endoscopic surveillance in many countries. A critical need exists to
identify effective and cost-effective strategies to address these clinical challenges in the U.S., as well
as globally. This proposed work builds upon prior GC work spanning GC and H. pylori simulation modeling
studies, GC secondary database analyses, and relevant methodologic and global cancer modeling
publications. The research team for this proposal has a vast collective experience in simulation and
comparative modeling for cancer control, clinical expertise across the spectrum of GC prevention and care,
and a demonstrated track record and commitment to informing cancer care and policy. The proposed three
modeling groups are well-positioned to perform and successfully complete the highly relevant project aims.
The overarching goal of the proposed research is to produce innovative and paradigm-shifting
changes to cancer care through a disparities-focused modeling approach targeting the most
vulnerable, high-risk populations that bear the greatest burden of GC in the US, and the world. We will
accomplish this goal by performing comparative modeling and completing the following aims: 1) Develop GC
simulation models to estimate GC outcomes for subgroups by race and ethnicity in the U.S.; 2) Assess the
impact of risk factor trends and primary prevention strategies on GC disparities; 3) Evaluate targeted
secondary prevention strategies for reducing early onset-related mortality and GC disparities, and 4) Adapt the
models to evaluate GC prevention policies in the global setting.
胃癌(GC),特别是胃腺癌,是最常见的癌症,第三种
全球癌症死亡的主要原因,已被世界卫生归类为被忽视的癌症
组织。在美国,存在鲜明的差异,黑人,西班牙裔和亚洲人有近两个 -
与白人相比,折叠出从GC发育或死亡的更大风险,反映了危险因素的差异,例如
作为幽门螺杆菌(H. Pylori)感染和吸烟,以及获得初级预防和护理。
几个因素正在改变预防GC的景观,包括对疾病的更好理解
自然历史,预防前瞻性研究的新证据,并预期了随机的结果
对照试验。由于早期的GC检测可以通过允许治疗手术或无创侵入性提高生存率
内窥镜切除,新的针对GC预防方法有可能明显改善
人口健康并减少美国境内的GC差异,尽管幽门螺杆菌一直是
迄今
全世界都强调了对弱势群体优化幽门螺杆菌的屏幕和治疗干预措施的需求
组。一种方法是针对高危个体的内窥镜筛查。例如,发现的人
具有胃肠肠化生,这是与胃的高进展风险相关的前体病变
建议在许多国家进行肿瘤进行内窥镜监测。存在关键需求
确定有效且具有成本效益的策略,以应对美国的这些临床挑战
全球。这项提出的工作是建立在跨GC和H. Pylori模拟建模的先前GC工作的基础上的。
研究,GC辅助数据库分析以及相关的方法论和全球癌症建模
出版物。该提案的研究团队在模拟和
癌症控制的比较模型,跨GC预防和护理范围的临床专业知识,
以及对癌症护理和政策告知的往绩和承诺。提议的三个
建模小组的性能很好,可以成功完成高度相关的项目目标。
拟议的研究的总体目标是产生创新和范式转移
通过以差距为中心的建模方法来改变癌症护理
脆弱的高风险人口承担美国和世界的最大负担。我们将
通过执行比较建模并完成以下目的来实现这一目标:1)开发GC
仿真模型以美国的种族和种族为子群体估算GC结果; 2)评估
风险因素趋势和主要预防策略对GC差异的影响; 3)评估目标
降低早期开始相关死亡率和GC差异的次要预防策略,以及4)适应
在全球环境中评估GC预防政策的模型。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Chin Hur其他文献
Chin Hur的其他文献
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{{ truncateString('Chin Hur', 18)}}的其他基金
Domain-Knowledge Informed Deep Learning for Early Detection of Pancreatic Cancer
基于领域知识的深度学习用于胰腺癌的早期检测
- 批准号:
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Optimal Colorectal Cancer Surveillance Strategy for Lynch Syndrome by Genotype
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Optimal Colorectal Cancer Surveillance Strategy for Lynch Syndrome by Genotype
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$ 91.92万 - 项目类别:
Comparative modeling of gastric cancer disparities and prevention in the US and globally
美国和全球胃癌差异和预防的比较模型
- 批准号:
10705668 - 财政年份:2021
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Domain-Knowledge Informed Deep Learning for Early Detection of Pancreatic Cancer
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