Combination of chronic conditions determining clinical relevance and resource use

描述(由申请人提供)：慢性疾病经常并存，五分之一的慢性病患者还表现出活动受限。此外，由于共同的病理生理学、药物-药物相互作用或药物-疾病相互作用，慢性疾病老年人也可能出现老年综合征，如跌倒、认知障碍或尿失禁。我们小组已经证明，慢性疾病(CC)与功能受限(FL)和/或老年综合征(GS)的结合，而不是CCS本身，与不良的健康结果(一般/较差的健康状况、两年的健康下降和死亡率)有关。然而，对于对临床和资源管理有最大影响的条件的具体组合--在CC、FL和GS这三种广泛的标准内和跨这些标准--我们知之甚少。依靠统计学习技术，并使用1992-2010年相关的健康和退休研究(HRS)和医疗保险数据，我们的目标是根据a)频率；b)健康结果；c)医疗服务利用；以及d)机构和非机构成本，确定CC、FL和GS的具体组合并进行排名 关心。我们的医疗服务利用指标包括急性(急诊科或医院入院和住院天数)、急性后(熟练护理设施、家庭健康护理或临终关怀)和长期护理。此外，我们将检查多种慢性病(MCC)组合和结果在不同年龄、性别、种族/民族和社会经济地位的老年人亚群中的差异，以及按服务收费的联邦医疗保险受益人和管理保健参与者之间的差异。这项研究的创新之处在于，它不仅关注CCS，还考虑了特定CC(S)与FL和GS的存在和严重程度对老年人健康结局、卫生服务使用和费用的影响。我们对HRS-Medicare数据的使用将提供一个独特的视角，将HRS中有关此类相关指标的丰富原始数据、认知状态(按严重程度)与Medicare索赔文件中的利用率数据相结合。此外，我们依靠最先进的统计学习方法，在确定与医疗保健使用和结果相关的临床条件的特定组合方面，理解数据所说的话。这一发现将在研究和临床实践中具有重要意义。在方法上，我们的研究将强调在检查健康结果和资源使用时更细微地确定MCC的重要性。从临床的角度来看，这些发现将表明在对MCC患者进行特征描述时，更全面和个性化的方法的重要性。此外，随着我们确定MCC患者亚群中预后不佳和资源使用率高的风险，将有可能设计有针对性的干预措施，以更好地满足这些患者的医疗保健需求，并减少医疗保健的利用。

DESCRIPTION (provided by applicant): Chronic conditions often co-occur, and one in five individuals with chronic illness also presents with activity limitations. In addition, because of shared pathophysiology, drug-drug interactions or drug-disease interactions, chronically ill older individuals may also present with geriatric syndromes, such as falls, cognitive impairment, or urinary incontinence. Our group has demonstrated that it is the combination of chronic conditions (CC) together with functional limitations (FL) and/or geriatric syndromes (GS), rather than CCs alone, that are associated with unfavorable health outcomes (fair/poor health status, two-year health decline, and mortality). However, little is known about the specific combinations of conditions - within and across the broad rubrics of CC, FL, and GS - that have the greatest implications for clinical and resource management. Relying on statistical learning techniques, and using the 1992-2010 linked Health and Retirement Study (HRS) and Medicare data, we aim to identify and rank specific combinations of CC, FL, and GS according to a) frequency; b) health outcomes; c) health services utilization; and d) institutional and non-institutional cost of care. Our metrics for health services utilization include acute (admission to the emergency department or to the hospital and the number of inpatient days), post-acute (skilled nursing facility, home health care, or hospice), and long-term care. Furthermore, we will examine variations in multiple chronic conditions (MCC) combinations and outcomes across subgroups of older adults by age, sex, race/ethnicity, and socioeconomic status, as well as between fee-for-service Medicare beneficiaries and managed care enrollees. This study is innovative, in that, rather than focusing on CCs alone, it accounts for the impact of specific CC(s) in conjunction with presence and severity of FL and GS, on health outcomes, health services use and costs in older adults. Our use of the HRS-Medicare data will offer a unique perspective by combining the rich primary data on such pertinent measures cognitive status, by severity, from the HRS with utilization data from Medicare claims files. In addition, we rely on state-of-the-art approach of statistical learning to understand "what the data says" with regard to identifying specific combinations of clinical conditions in relation to health care use and outcomes. The findings will have important implications in both research and clinical practice. Methodologically, our study will highlight the importance of a more nuanced approach to defining MCC when examining health outcomes and resource use. From a clinical perspective, the findings will show the importance of a more comprehensive and personalized approach when characterizing patients with MCC. In addition, as we identify subgroups of patients with MCC with high risk for poor outcomes and high resource use, it will be possible to design targeted interventions to better meet these patients' health care needs and to curtail health care utilization.