Understanding the Multilevel Drivers of Liver Cancer Disparities

项目摘要/摘要 从2000年到2014年，肝细胞癌或称肝癌的发病率每年增长近4%，而大多数 美国的癌症呈下降趋势。肝癌对少数族裔/民族群体的影响不成比例 他们的诊断率大约是非西班牙裔白人的两倍。告知初级预防 减少肝癌风险差异的战略，我们需要确定井-水的相对贡献。 已确定的和正在出现的(例如，乙肝病毒、丙型肝炎病毒、酒精、吸烟、肝硬变、非酒精性脂肪肝、 代谢紊乱、糖尿病、艾滋病毒感染)和新奇(如药物、合并症、邻里关系 属性)风险因素导致了这些差异。为二级和三级预防战略提供信息，以减少 在肝癌负担方面的差异，我们需要了解有助于肝癌监测的多水平因素 差距。填补这些知识差距需要一项强有力的高质量研究，并为以下方面丰富样本 少数族裔。因此，我们建议利用现有的多学科协作来开发 集成数据集，包括与基于人口的州癌症相关的电子健康记录(EHR)数据 登记册数据和地理空间背景数据。这一多层次资源将包括近230万人的数据 来自三种医疗保健系统的个人(混合支付者、综合医疗保健、联邦合格医疗保健 在加利福尼亚州和夏威夷)，从而在医疗保健环境中提供多样性，并丰富种族/民族 少数民族：59,400名黑人，189,500名西班牙裔，441,700名亚裔美国人/夏威夷原住民/太平洋人 岛民(AANHPI)。有了这一资源，我们的具体目标是：(1)评估已建立的 和Emerging研究这些因素在多大程度上独立地和共同地对种族/民族作出贡献 肝细胞癌风险的差异；(2)发现新的风险因素并评估它们对肝细胞癌风险的相对重要性；以及(3) 根据对肝细胞癌的监测评估种族/民族差异，并检查 这些差异可归因于可修改的个人因素、临床医生因素、系统因素和邻里因素(目标3)。 对于目标1，使用前瞻性数据，我们将评估风险因素及其贡献的相对重要性。 使用因果推断方法对肝癌风险的种族/民族差异进行研究。对于目标2，我们将应用创新 使用机器学习方法识别新因素并验证它们与肝癌风险的关联 目标1的建模策略。对于目标3，我们将使用多水平广义线性回归来研究 患者、临床医生、机构和地理因素导致肝细胞癌监测的差异。vt.给出 性别和年龄/出生年龄对肝癌风险的重要性，这些社会决定因素将被一起考虑 使用交叉方法处理种族/民族问题。通过应用多层次框架来了解 多层次的生物、临床和社会因素导致肝癌发病率和死亡率的差异 通过监测，拟议的研究将确定可转化为临床和 社区环境，以协作确定战略，以改善肝癌的种族/民族差异。

Project Summary/Abstract From 2000-2014, hepatocellular carcinoma, or HCC, incidence rates increased nearly 4% per year, while most cancers in the United States were on the decline. HCC disproportionately impacts minority racial/ethnic groups who are diagnosed at rates approximately twice that of non-Hispanic Whites. To inform primary prevention strategies that will reduce disparities in HCC risk, we need to determine the relative contribution of well- established and emerging (e.g., hepatitis B virus, hepatitis C virus, alcohol, smoking, cirrhosis, NAFLD, metabolic disorders, diabetes, HIV infection), and novel (e.g., medications, comorbidities, neighborhood attributes) risk factors to these disparities. To inform secondary and tertiary prevention strategies to reduce disparities in HCC burden, we need to understand the multilevel factors that contribute to HCC surveillance disparities. Answering these gaps in knowledge requires a robust high-quality study with a sample enriched for racial/ethnic minorities. Thus, we propose to leverage existing multi-disciplinary collaborations to develop an integrated dataset that includes electronic health records (EHR) data linked to population-based state cancer registry data and geospatial contextual data. This multilevel resource will include data on nearly 2.3 million individuals from three healthcare systems (mixed payer, integrated healthcare, federally qualified health centers) in California and Hawaii, thus providing diversity in healthcare settings and enrichment for racial/ethnic minorities: 59,400 are Black, 189,500 are Hispanic, and 441,700 are Asian American/Native Hawaiian/Pacific Islander (AANHPI). With this resource, we specifically aim to: (1) assess the relative importance of established and emerging examine the extent to which these factors independently and jointly contribute to racial/ethnic disparities in HCC risk; (2) discover novel risk factors and assess their relative importance to HCC risk; and (3) assess racial/ethnic disparities in adherence with surveillance for HCC as well as examine the extent to which these disparities are attributable to modifiable individual-, clinician-, system-, and neighborhood factors (Aim 3). For Aim 1, using prospective data, we will assess the relative importance of risk factors and their contribution to racial/ethnic disparities in HCC risk with causal inference methods. For Aim 2, we will apply innovative machine learning methods to identify novel factors and validate their associations with HCC risk using modeling strategy from Aim 1. For Aim 3, we will use multilevel generalized linear regression to investigate the patient, clinician, institutional and geographic factors that contribute to disparities in HCC surveillance. Given the importance of sex and age/birth cohort for HCC risk, these social determinants will be considered together with race/ethnicity using an intersectional approach. By applying a multilevel framework to understand how biological, clinical, and social factors at multiple levels contribute to HCC disparities in incidence and surveillance, the proposed study will identify modifiable factors that can be translated to the clinical and community settings to collaboratively identify strategies to ameliorate racial/ethnic disparities in HCC.