Population-Based Outcomes Research for Rheumatoid Arthritis: Rural Health Disparities

基于人群的类风湿关节炎结果研究：农村健康差异

• 批准号: 10929530
• 负责人: Cynthia S Crowson
• 金额: $ 15万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10929530
• 关键词: Address; Age; Algorithms; American; Antirheumatic Agents; Area; Arizona; Caring; Catchment Area; Characteristics; Classification; Code; Consumption; Continuity of Patient Care; County; Data; Data Set; Databases; Development; Diagnosis; Diagnostic; Disease remission; Disparity in diagnosis; Early Diagnosis; Early identification; Early treatment; Electronic Health Record; Emergency department visit; Ensure; Epidemiology; Ethnic Origin; European; Florida; Goals; Grant; Health Services Accessibility; Health care facility; Healthcare Systems; Hospitalization; Intervention; Joints; Knowledge; Manuals; Medical; Minnesota; Obesity; Outcome; Outcomes Research; Patients; Peptide antibodies; Persons; Pharmacotherapy; Population; Positioning Attribute; Pragmatic clinical trial; Primary Care; Provider; Qualifying; Race; Reporting; Research; Rheumatoid Arthritis; Rheumatoid Factor; Rheumatology; Rural; Rural Community; Rural Hospitals; Rural Population; Site; Smoking; Specialist; Symptoms; Testing; Time; Training; Travel; Triage; Wisconsin; Woman; Work; access disparities; artificial intelligence algorithm; clinical decision support; clinical practice; cohort; college; comorbidity; cyclic citrullinated peptide; disparity reduction; experience; health care delivery; health care disparity; health care service utilization; human old age (65+); improved; improved outcome; joint injury; medical specialties; mortality; opioid use; patient subsets; population based; primary care setting; public health relevance; rheumatologist; rural area; rural dwellers; rural health disparities; rural healthcare; rurality; sex; support tools; unnecessary treatment; urban area

PROJECT SUMMARY/ABSTRACT In rheumatoid arthritis (RA), delaying initiation of treatment for ≥12 weeks is associated with irreversible joint damage due to greater difficulty in achieving remission, but navigating the hurdles of the US healthcare system in this short timeframe can be challenging. Time-consuming logistical steps must be carried out to be evaluated by a qualified specialist, establish a diagnosis, and start antirheumatic drug therapy. These difficulties are exacerbated for rural residents due to the closure of rural hospitals nationwide extending the distance to healthcare facilities and the time to travel and obtain care. Additionally, there is very little information about the timing and accuracy of RA diagnosis and their associated effects on outcomes for rural residents of the US. The growing rheumatology workforce shortage further aggravates these issues, as <10% of rheumatologists practice in rural or micropolitan areas yet 30% of the US population lives in these areas. Our goal is to improve outcomes in patients with RA by reducing healthcare disparities. Our aims are to (1) identify rural healthcare disparities in diagnosis and treatment of early RA, (2) to develop and validate an AI algorithm to enable early identification of RA, and (3) to assess long-term outcomes in patients with RA living in rural vs urban areas compared to those without RA. We are uniquely positioned to address these objectives as ours is the only population-based, longitudinal RA inception cohort in the US, and we will extend our cohort to the expanded Rochester Epidemiology Project catchment area, comprising 27 mixed rural-urban counties in MN and WI (60% rural). This area includes 73 rural communities where 17 medical facilities have closed in the past 5 years. In addition, we have the only well- curated database of patients with incident RA, as we manually screen all potential cases of RA to ensure they meet the American College of Rheumatology/ European Alliance of Associations for Rheumatology (ACR/EULAR) classification criteria for RA. This is essential for RA research because the diagnostic codes for RA (especially seronegative RA) are often inaccurate. This research will provide real-world evidence regarding the magnitude of healthcare disparities in rural residents with RA. Our AI algorithm to facilitate early recognition of RA will improve clinical practice in three meaningful ways, by: 1) reducing underdiagnosis of RA in rural primary care settings, 2) facilitating timely initiation of anti-rheumatic therapies to reduce joint damage and improve long-term outcomes, and 3) optimizing referrals to rheumatology specialty care to reduce the impact of the workforce shortage. This work will be foundational for the development of a pragmatic clinical trial testing implementation of a clinical decision support tool to reduce disparities in early identification of RA. Furthermore, evaluating long-term outcomes and identifying patient subgroups with the worst outcomes is an important step toward developing interventions to bridge these gaps in healthcare delivery.

项目摘要/摘要 在类风湿性关节炎(RA)中，将≥治疗推迟12周与不可逆转的关节有关 损害是由于实现缓解的难度更大，但克服了美国医疗体系的障碍 在这么短的时间内，这可能是具有挑战性的。必须执行耗时的后勤步骤才能进行评估 由合格的专科医生进行诊断，并开始抗风湿药物治疗。这些困难是 由于全国乡村医院的关闭，农村居民的情况进一步恶化，距离延长到 医疗设施以及旅行和获得护理的时间。此外，有关这方面的信息很少 类风湿性关节炎诊断的时间和准确性及其对美国农村居民预后的相关影响。这个 日益严重的风湿病劳动力短缺进一步加剧了这些问题，因为10%的风湿病医生执业 在农村或小城市地区，仍有30%的美国人口生活在这些地区。我们的目标是改善结果 通过减少医疗保健差距，改善类风湿关节炎患者的生活质量。我们的目标是(1)确定农村医疗保健方面的差距 早期类风湿关节炎的诊断和治疗，(2)开发和验证人工智能算法，以使早期识别 RA，以及(3)评估居住在农村地区的RA患者与城市地区的RA患者的长期结果 没有RA。 我们在实现这些目标方面具有得天独厚的优势，因为我们是唯一一个以人口为基础的纵向RA 美国的初始队列，我们将把队列扩展到扩大的罗切斯特流行病学项目 集水区，由西北和西北部的27个城乡混合县组成(60%为农村)。这一地区包括73个农村 在过去5年中有17家医疗机构关闭的社区。另外，我们还有一口井- 精心策划的RA事件患者数据库，因为我们手动筛选所有潜在的RA病例，以确保他们 美国风湿病学会/欧洲风湿病协会联盟 (ACR/EULAR)RA分类标准。这对于类风湿关节炎研究至关重要，因为 RA(尤其是血清阴性RA)通常不准确。这项研究将提供关于以下方面的真实证据 患有类风湿关节炎的农村居民的医疗保健差距的大小。我们的人工智能算法有助于早期识别 风湿性关节炎的治疗将在三个有意义的方面改善临床实践：1)减少农村风湿性关节炎的漏诊 初级保健环境，2)促进及时开始抗风湿治疗，以减少关节损害和 改善长期结果，以及3)优化风湿病专科护理的转诊，以减少 劳动力短缺。这项工作将为开发实用的临床试验测试奠定基础 实施临床决策支持工具，以减少类风湿关节炎早期识别方面的差异。此外， 评估长期结果并确定预后最差的患者亚组是重要的一步 制定干预措施，以弥合医疗保健提供方面的这些差距。