Comparative Effectiveness Research Diffusion and Mental Health Care Disparities

描述(由申请人提供)：比较有效性研究(CER)比较不同治疗方法的益处和风险。然而，人们对2010年《平价医疗法案》中设想的大规模实施《经济、社会和文化权利法案》对各种族/族裔群体的医疗保健公平的影响知之甚少。确定FDA风险警告对精神药物使用差异的影响提供了一个平台，了解CER在某些情况下将如何影响差异。视差方法通常没有考虑到具体的 关于有效性和安全性的建议和警告。随着ACA的到来和对持续存在的差异的持续关注，继续进行差异研究是当务之急 发展以纳入从CER中吸取的经验教训。我们利用警告和咨询中的时间趋势来了解扩散方面的差异，目的是确定可能避免将CER纳入常规实践的负资产后果的政策。具体地说，我们1)按种族/民族跟踪青少年中抗抑郁药和青少年和成人中抗精神病药物使用的变化，因为它们受到CER生成的风险警告和建议的影响；2)评估FDA警告中的特定信息是否影响了a)CER生成证据推荐的治疗(例如，氟西汀使用和抗精神病药物使用者的代谢筛查)中的差异趋势；以及b)潜在有害的精神药物使用(例如，奥氮平的使用)；以及3)确定提供者特征和HMO注册如何作为通过健康风险警告进行CER差异传播的机制。我们执行医学研究所(IOM)对医疗保健差距的定义，以捕捉随着时间的推移差距的趋势，覆盖与抗抑郁药和抗精神病药物相关的警告和建议的时间表。我们通过将总体药物使用的差异分解为FDA警告中提到的具有特定风险或好处的特定类别的差异，来调整IOM的方法。如果我们发现在风险警告和建议的传播方面存在种族/民族差异，这表明政策制定者应该专注于提高少数族裔对CER的接受程度。如果我们确定某些提供者和HMO成员会影响传播差异，这将为政策制定者提供建议，以避免将CER纳入常规实践的负资产后果。

DESCRIPTION (provided by applicant): Comparative Effectiveness Research (CER) compares benefits and risks of different treatments. However, little is known about the impact of large-scale CER implementation as conceived in the 2010 Affordable Care Act (ACA) on equity in health care across racial/ethnic groups. Identifying the influence of FDA risk warnings on disparities in psychotropic drug use provides a platform to understand how CER will influence disparities in certain situations. Disparity methods have typically not taken into account specific recommendations and warnings on effectiveness and safety. With the advent of the ACA and continued concern over persistent disparities, it is imperative that disparities research continues to evolve to incorporate lessons learned from CER. We capitalize upon time trends in warnings and advisories to understand disparities in diffusion with the intention of identifying policies tht might avert the negative equity consequences of incorporating CER into routine practice. Specifically, we 1) Track changes in antidepressant use among youth and antipsychotic drug use among youth and adults by racial/ethnic group as they are influenced by CER-generated risk warnings and advisories; 2) Assess whether specific information in FDA warnings influenced disparity trends in a) treatments recommended by CER-generated evidence (e.g., fluoxetine use and metabolic screening for antipsychotic users); and b) psychotropic drug use that is potentially harmful (e.g., olanzapine use); and 3) Identify how provider characteristics an HMO enrollment act as mechanisms that underlie the differential diffusion of CER via health risk warnings. We implement the Institute of Medicine (IOM) definition of healthcare disparities in order to capture trends in disparities over time, overlaying timelines of warnings and recommendations related to antidepressant and antipsychotic drugs. We adapt IOM methods by decomposing disparities in overall drug use into disparities in specific categories of use mentioned by FDA warnings as carrying particular risk or benefit. If we find racial/ethnic disparities in diffusion of risk warnings and advisories, this suggests policymakers should focus on improving uptake of CER among minorities. If we identify that certain providers and HMO membership influences disparities in diffusion, this will lead to recommendations for policymakers of actionable policies to avert the negative equity consequences of incorporating CER into routine practice.