Using Geocoded Socioeconomic Data to Enhance Pediatric Risk Adjustment Methods

描述（由申请人提供）：社会经济（SE）因素强烈影响儿童健康和保健。平均而言，低SE背景的儿童比高SE背景的儿童一开始就有更多的健康问题，并且在整个童年时期面临来自家庭和社区的更多健康风险。寻求提供高质量护理的医生和保险公司必须根据儿童的SE背景量身定制医疗服务。然而，照顾低SE背景儿童所增加的复杂性并没有得到补偿。因此，他们面临着避免这些患者的动机，并且对se相关的健康问题认识不足或治疗不足。认识到低SE儿童所需要的额外努力的支付方式可能会提高医生和保险公司照顾和保险这些人群的意愿，并提高向他们提供的护理质量。风险调整（RA）是卫生保健系统用于分配资源的基于统计的方法，以便那些照顾复杂病人的人比那些不照顾复杂病人的人获得更多的资源。现有的儿科类风湿性关节炎方法不发达且效果不佳，预测儿科医疗保健支出仅为成人支出的一半。此外，我们不知道有任何儿科研究评估改进RA算法是否会减少对“脱脂”健康患者或“抛弃”病情较重患者的激励（尽管存在许多成人研究）。我们的具体目标是：(1)评估基于地理位置的SE信息是否提高了RA算法预测儿科支出的能力；(2)模拟在RA算法中包含基于地理位置的SE信息在多大程度上降低了提供者避免低SE背景儿童的动机。我们将使用2007-2010年马萨诸塞州蓝十字蓝盾（BCBSMA）的索赔数据和2006-2010年美国人口普查局美国社区调查（ACS）的人口普查区数据进行一系列的3个横断面小组研究。我们的研究人群将是约25,000名参加BCBSMA保险的儿童，并由波士顿儿童医院（BCH）附属的初级保健医生通过位于马萨诸塞州的72个私营社区办事处和2个BCH拥有的医院实践来护理。这些数据集包含从患者地址对SE变量进行“地理编码”和计算年度医疗支出总额（即在所有机构提供的服务，而不仅仅是BCH）所需的所有信息。初步分析表明，患者群体在地理上和社会经济上是多样化的。了解基于地理位置的SE信息是否可以改善儿科RA模型，将填补儿科RA领域的关键知识空白，并促进我们对如何更好地将医疗保健支付与儿科患者复杂性结合起来的理解。这项研究将促进正在进行的卫生系统和政策努力，旨在确保低SE背景的儿童能够获得保险和医疗保健，并缩小儿童健康和医疗保健方面的差距。

DESCRIPTION (provided by applicant): Socioeconomic (SE) factors strongly affect child health and healthcare. On average, children from low SE backgrounds begin life with more health problems, and face more health risks from their homes and neighborhoods throughout childhood than do children from high SE circumstances. Physicians and insurers seeking to deliver high quality care must tailor medical services to children's SE background. However, they are not compensated for the added complexity of caring for children from low SE backgrounds. Consequently, they face incentives to avoid these patients, and under-recognize or under-treat SE-related health issues. Payment methods that recognize the added effort that low SE children require may improve the willingness of physicians and insurers to care for and insure these populations and improve the quality of care delivered to them. Risk adjustment (RA) is the statistically based approach used by the healthcare system to allocate resources so that those who care for complex patients are given more resources than those who do not. Existing pediatric RA methods are underdeveloped and perform poorly, predicting pediatric health care spending only half as well as adult spending. Further, we are not aware of any pediatric studies evaluating whether improving RA algorithms reduces incentives to "cream-skim" healthier patients or "dump" sicker ones (though many adult studies exist). Our Specific Aims are (1) to evaluate whether geographically based SE information improves the ability of RA algorithms to predict pediatric spending; (2) to simulate the extent to which including geographically based SE information in RA algorithms reduces incentives for providers to avoid children from low SE backgrounds. We will conduct a series of 3 cross-sectional panel studies using claims data from Blue Cross Blue Shield of Massachusetts (BCBSMA) 2007-2010 and census tract data from the U.S. Census' American Community Survey (ACS) 2006-2010. Our study population will be the ~25,000 children insured by BCBSMA and cared for by primary care physicians affiliated with Boston Children's Hospital (BCH) through 72 privately-owned community-based offices located across Massachusetts and 2 BCH-owned hospital-based practices. These datasets contain all the information needed to "geocode" SE variables from patient addresses and to calculate total annual medical spending (i.e., services provided at all institutions, not just BCH). Preliminary analyses show that the patient population is geographically and socioeconomically diverse. Knowing whether geographically based SE information can improve pediatric RA models will fill a critical knowledge gap in the pediatric RA field, and advance our understanding of how to better align healthcare payments with pediatric patient complexity. The study will facilitate ongoing health system and policy efforts aimed at ensuring that children from low SE backgrounds have access to insurance and medical care and narrowing disparities in child health and healthcare.