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

点击翻译按钮获取中文摘要

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.