Age-specific Patterns of Healthcare Utilization in NMOSD
Melissa Wright1, Ka-Ho Wong2, Yibing Zhang1, Tracy Schaffer1, Regina Farley1, Tammy Smith3, John Rose3, Stacey Clardy1
1University of Utah, 2U of U Neurology Clinic, 3Imaging and Neurosciences Center
Objective:

Evaluate for age-specific patterns in healthcare utilization among pediatric, young adult, and adult patients with neuromyelitis optica spectrum disorder (NMOSD).  

Background:

NMOSD is a relapsing demyelinating disease with high risk of cumulative neurologic disability if untreated. Lifelong immunotherapy is essential to prevent attacks, but patients may have ongoing neurologic disability and symptoms affecting quality of life. Young adults with chronic disease have shown different patterns of healthcare utilization, and this is an area of needed investigation in NMOSD. 

Design/Methods:

A retrospective chart review was conducted on patients with NMOSD of all ages diagnosed and actively followed by an autoimmune neurologist at the University of Utah or Primary Children’s Hospital. Demographics and data regarding healthcare utilization were collected. 

Results:

28 patients with NMOSD were identified (79% female, 68% white). Median current age of the cohort was 42 years (range 7-78), with a median age of diagnosis of 35 years (range 6-68). Patients of all ages experienced delays in diagnosis, access to an NMOSD specialist, and initiation of treatment. Over the past year, 23% of patients <40y had at least one “no showed” appointment, 54% did not have follow up for NMOSD in the planned time frame, and 38% had not pursued referral and/or symptomatic treatment recommendations. In contrast, missed or delayed appointments and referrals were rare in patients ages 40 or older.  

Conclusions:

Delays in diagnosis, access to an NMOSD specialist, and treatment initiation were common across all age groups. In patients <40y, over half had delayed follow up. Young patients with NMOSD may be at risk for unique challenges related to healthcare utilization, and understanding these patterns may help develop targeted interventions to improve care. 

 

10.1212/WNL.0000000000217498
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