Linking State Medicaid and Congenital Heart Surgical Registry Data: Building Capacity to Assess Disparities in Longitudinal Outcomes and Value for Children with Congenital Heart Disease

项目摘要 先天性心脏病是美国最常见和资源密集型的出生缺陷 在发病率和死亡率较高的国家。据估计，它们将影响100多万美国儿童和140万儿童 每年造成约200,000个寿命年的损失和60亿美元的住院急性护理费用。 众所周知，在短期成果和资源利用方面存在重大差异。我们已经证明了 来自低收入社区的儿童经风险调整后住院死亡率高18%，高出7% 住院时间更长，需要的围手术期资源比收入较高的孩子多7% 社区，即使在控制了种族的影响，公共和私人保险提供商，以及 这些儿童最终接受手术修复的医院。对长期结果和 卫生支出有限，推动卫生不平等的机制尚不清楚。超过50% 患有先天性心脏病的儿童被医疗补助覆盖。我们已经与NY部门合作 并拥有纽约州超过15年的所有医疗补助患者遇到的干净、有效的数据。 我们开发了提取本地持有的临床注册数据并将其链接到Medicaid文件的方法，并 建立了一支由儿科心脏病专家、心脏外科医生、卫生服务研究人员组成的跨学科团队， 健康经济学家和纽约卫生部高级领导确定医疗保健服务的各个方面 这可以通过中央或州一级的干预措施来减少不平等。我们建议将 来自纽约各州的儿科心脏外科临床登记数据，纵向医疗补助申请，人口普查 局里的数据，地理编码的数据，以及国家死亡指数。我们将使用这些数据来完成 以下目标：1)我们将定义10年风险分层的多维结果(死亡率和发病率) 以及纽约州各地接受先天性心脏病手术的儿童的相关医疗支出； 2)我们将为患有先天性心脏病的儿童开发新的纵向风险模型，以适应社会 健康的决定因素，以及3)我们将医疗保健可获得性的维度作为健康的可修改驱动因素进行测试 纽约先天性心脏病儿童在医疗补助方面的不平等。实现我们的目标将会 1)建立以人口为基础的纵向成果和卫生支出综合资源 研究，不仅包括住院数据，还包括门诊、急诊室、药房、康复、 近一半的家庭医疗保健、教育记录和社区层面的社会健康决定因素 在纽约州接受先天性心脏病手术的儿童，2)制定一种调查方法 适用于多个人群的长期结果和价值，3)评估接入的结构，如 健康的社会决定因素的可修改的中介因素，以及4)使我们能够将这些发现转化为政策 和护理改革，以减少健康不平等。

Project Summary Congenital heart defects are the most common and resource intensive birth defects managed in the United States with high morbidity and mortality. They are estimated to effect over 1 million US children and 1.4 million US adults and to result in ~200,000 life-years lost and >$6 billion in inpatient acute care costs per year. Significant disparities are known to exist in short-term outcomes and resource utilization. We have shown that children from low income neighborhoods have 18% higher risk-adjusted odds of in-hospital mortality, have 7% longer lengths-of-stay, and require 7% more perioperative resources than do children from higher income neighborhoods, even after controlling for the effects of race, public versus private insurance provider, and the hospitals at which these children ultimately receive surgical repair. Research on long-term outcomes and health expenditures is limited and mechanisms driving health inequities remain unknown. Greater than 50% of children with congenital heart disease are covered by Medicaid. We have partnered with the NY Department of Health and have clean, validated data for all Medicaid patient encounters over 15 years across NY State. We have developed methods of abstracting and linking locally-held clinical registry data to Medicaid files and have built an interdisciplinary team of pediatric cardiologists, cardiac surgeons, health services researchers, health economists, and NY Department of Health senior leadership to identify dimensions of healthcare access that could be targeted through center- or state-level interventions to reduce inequities. We propose linking pediatric cardiac surgical clinical registry data from across NY State, longitudinal Medicaid claims, Census Bureau data, geocoded data, and the National Death Index. We will use these data to accomplish the following Aims: 1) We will define 10-year risk-stratified, multi-dimensional outcomes (mortality and morbidities) and associated health expenditures for children undergoing congenital heart surgery across New York State; 2) we will develop novel longitudinal risk models for children with congenital heart disease that adjust for social determinants of health, and 3) we will test dimensions of healthcare access as modifiable drivers of health inequities among children in New York with congenital heart disease on Medicaid. Achieving our aims would 1) establish a comprehensive, population-based resource for longitudinal outcomes and health expenditure research, inclusive of not only in-patient data, but also out-patient, emergency room, pharmacy, rehabilitation, home healthcare, education records, and neighborhood-level social determinants of health for nearly half of all children undergoing congenital heart surgery in NY State, 2) generate a methodology for investigations on long-term outcomes and value applicable across multiple populations, 3) assess constructs of access as modifiable mediators of social determinants of health, and 4) position us to translate these findings into policy and care changes to reduce health inequities.