Technology Diffusion, Health Outcomes, and Healthcare Expenditures

描述：在以前的研究中，人们发现医疗支出和健康结果都有很大的差异，两者之间几乎没有相关性。这些研究仅限于横断面分析，对这些模式产生的动态过程知之甚少。一种假设是，不同地区技术扩散速度的差异，无论是高效治疗(对健康结果有很大影响)还是价值未知的昂贵治疗(对支出有很大影响)，都可以解释观察到的支出和结果的横截面模式。在这项提案中，Aim 1试图更好地了解高效医疗保健在糖尿病患者中的传播情况，例如用于控制血糖的血红蛋白A1c(HbA1c)测试。研究小组将利用美国每个医生的国家Doximity数据库，以及医生-医院网络(PHN)和医生社交网络的信息，测试为什么HbA1c在一些地区传播得如此迅速(以及在所有种族和民族中)，而在其他地区却没有。他们还将测试HbA1c的更快扩散是否会降低神经病、视网膜病和截肢率。目标2侧重于推广一般有益的治疗，但在治疗实际上可能伤害特定类型的患者的情况下。两个例子被考虑：植入型心律转复除颤器(ICD)的快速增长，以及新型和昂贵的抗凝剂--达比卡特兰、阿皮沙班和利伐沙班的增长。AIM 3研究与扩散相反--“淘汰”或停止使用--询问医生-医院网络和地区如何缩减新发现的对患者亚群没有价值的治疗。该提案考虑了两种具体的治疗方法：大幅减少颈动脉内膜切除术(手术和支架)，以及在2007年发表的一篇文章显示严重的心血管风险后，减少了抗糖尿病药物罗格列酮(文迪雅)的使用。在这些情况下，对于不太合适的患者来说，最有效的清除模式应该经历最大的降幅。目标4审查了具有未知甚至不利后果的治疗的扩散，例如在一些地区(但不是其他地区)ICU床位容量的快速增长。研究小组将研究“非医疗”治疗的网络和扩散模式--受利润驱动的非法行为，对患者没有任何好处，其中一个例子是2002-2005年间免疫球蛋白输注的上升和下降。最后，研究小组将使用这四个目标的结果来回到中心假设：观察到的特定治疗扩散的差异能否解释观察到的健康结果和支出地区差异的模式？

DESCRIPTION: In previous research wide variations have been found in both healthcare spending and in health outcomes, with little correlation between the two. These studies were limited to cross-sectional analysis, and tell little about the dynamic process by which these patterns arise. One hypothesis is that variation across regions in rates of technology diffusion, whether for highly effective treatments (with a large impact on health outcomes) or for expensive treatments with unknown value (with a large impact on expenditures), can explain the observed cross-sectional patterns of spending and outcomes. In this proposal, Aim 1 seeks to better understand the diffusion of highly effective healthcare such as hemoglobin A1C (HbA1c) tests for blood glucose control among diabetic patients. Using the national Doximity database on every physician in the U.S., along with information about physician-hospital networks (PHN) and physician social networks, the research team will test why HbA1c diffused so rapidly (and among all racial and ethnic groups) in some areas but not others. They will also test whether more rapid diffusion of HbA1c reduced rates of neuropathy, retinopathy, and amputation. Aim 2 focuses on the diffusion of generally beneficial treatments but where the treatment can actually harm specific types of patients. Two examples are considered: the rapid growth in implantable cardioverter defibrillators (ICDs), and the growth in new and expensive anticoagulants - dabigatran, apixaban and rivaroxaban. Aim 3 studies the opposite of diffusion - "exnovation" or a retreat from use - to ask how physician-hospital networks and regions scaled back on treatments newly found to have poor value for subgroups of patients. The proposal considers two specific treatments: the sharp reduction in carotid endarterectomy (both surgery and stents), and the decline in the use of Rosiglitazone (Avandia), an anti-diabetic drug, following a 2007 publication demonstrating serious cardiovascular risks. In these cases, the most effective exnovation patterns should experience the largest drop in use for the less appropriate patients. Aim 4 examines the diffusion of treatments with unknown or even adverse consequences, such as the rapid growth in some regions (but not others) in ICU bed capacity. The research team will study the network and diffusion patterns for "extramedical" treatments - illegal behavior motivated by profit and with no benefit for patients, with one example being the rise and fall of immunoglobulin infusions in 2002-2005. Finally, the research group will use results from these four aims to return to the central hypothesis: can observed differences in treatment-specific diffusion explain observed patterns in regional variations in health outcomes and spending?