The authors modeled how patient uptake and health care system capacity will affect the delivery of Alzheimer’s disease–modifying therapies. They expand on prior work by examining the role of primary care and U.S. geographic variation in capacity.

Modeling Early Detection and Geographic Variation in Health System Capacity for Alzheimer's Disease–Modifying Therapies

Jodi L. Liu, Lawrence Baker, Annie Chen, Jessie Wang, Federico Girosi

ResearchPublished Jan 30, 2024

Widespread availability of effective disease-modifying therapies (DMTs) would be a breakthrough in slowing the progression of early-stage Alzheimer’s disease (AD) to later stages of dementia. An AD DMT has received traditional approval from the Food and Drug Administration, and more candidates are on the horizon. The authors used a simulation model to assess patient demand and provider supply for the delivery of AD DMTs. They expanded on prior modeling by including the capacity of primary care practitioners (PCPs) who perform brief cognitive assessments and by utilizing county-level data.

The results suggest that engaging the primary care workforce in the diagnostic process would be vital to accelerate the delivery of AD DMTs, which will overwhelm neurology and geriatric practices that often already have wait lists for appointments. Under the base case assumptions, the states with the longest wait times are Alaska, Arkansas, Idaho, Mississippi, Montana, Nevada, Oklahoma, and Wyoming. Primary care models and telehealth models could help improve access to care in these areas. Further work is needed to evaluate how primary care–led models of care can widely and effectively evaluate and manage treatment for people with early-stage AD, as well as how technological advancements, such as improved biomarkers and computerized testing, can be integrated into workflows.

Key Findings

  • There is substantial variation in health care system capacity across the United States to detect, diagnose, and treat early-stage AD with DMTs.
  • The estimated wait times and the number of patients treated are sensitive to patient uptake of brief cognitive assessments.
  • Estimated average wait times vary by state and can be three times longer in rural areas than in urban areas.
  • Care models that enable PCPs to diagnose and evaluate patients for treatment eligibility would make the biggest impact on reducing wait times for specialists and increasing the number of people treated from 2025 through 2044 in the analysis. Improved triage of patients using blood-based biomarker tests could further reduce caseloads for specialists.
  • Widespread delivery of AD-modifying therapies will require a combination of strategies to communicate the value of detection and treatment to patients, integrate PCPs into the detection and diagnosis pathway, and address capacity disparities across the United States.

Topics

Document Details

Citation

RAND Style Manual
Liu, Jodi L., Lawrence Baker, Annie Chen, Jessie Wang, and Federico Girosi, Modeling Early Detection and Geographic Variation in Health System Capacity for Alzheimer's Disease–Modifying Therapies, RAND Corporation, RR-A2643-1, 2024. As of October 14, 2024: https://www.rand.org/pubs/research_reports/RRA2643-1.html
Chicago Manual of Style
Liu, Jodi L., Lawrence Baker, Annie Chen, Jessie Wang, and Federico Girosi, Modeling Early Detection and Geographic Variation in Health System Capacity for Alzheimer's Disease–Modifying Therapies. Santa Monica, CA: RAND Corporation, 2024. https://www.rand.org/pubs/research_reports/RRA2643-1.html.
BibTeX RIS

Research conducted by

This research was sponsored by Genentech and conducted within the Access and Delivery Program of RAND Health Care.

This publication is part of the RAND research report series. Research reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND research reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.