Cost-effectiveness estimates for Alzheimer’s drugs should consider effects on caregivers

Estimating treatment effects on caregivers can help determine the cost-effectiveness of treatments for Alzheimer’s disease, according to results of an economic evaluation published in JAMA Network Open.

“While no new pharmacotherapies for AD have been licensed since 2003, ongoing developments and advances in AD research raise the possibility of novel disease-modifying therapies in the near future,” Kouta Ito, MD, MS, of Meyers Primary Care Institute in Massachusetts, and colleagues wrote. “There is a need to assess the value of such treatments, and cost-effectiveness models can serve as a starting point for setting value-based prices and preparing the health care system to pay for new AD drugs.”

The researchers sought to examine how inclusion of various patient, caregiver and societal treatment-related factors altered cost-effectiveness estimates of a hypothetical disease-modifying AD treatment.

Using the Alzheimer Disease Archimedes Condition Event Simulator, a patient-level microsimulation model, Ito and colleagues simulated the prognosis of hypothetical patients with mild cognitive impairment drawn from the Alzheimer Disease Neuroimaging Initiative database. The simulated cohort included patients with scores of between 24 and 30 on the Mini-Mental State Examination and a global Clinical Dementia Rating scale of 0.5, with a required memory box score of at least 0.5, at baseline. The researchers conducted scenario analyses with variations for costs and quality of life inputs related to patients and caregivers. The analysis took place between June 15, 2019, and Sept. 30, 2020. Incremental cost-effectiveness ratio (ICER), assessed based on cost per quality-adjusted life-year (QALY) gained, served as the main outcome.

When using a health care sector perspective that included only individual patient health care costs, results showed an ICER of $192,000 per QALY gained for the hypothetical treatment, which decreased to $183,000 per QALY gained in a traditional societal perspective analysis that included patient non-health care costs. Upon including estimated caregiver health care costs, Ito and colleagues found the ICER remained almost entirely the same; however, including QALYs gained by caregivers correlated with a significant reduction in the ICER for hypothetical treatment in the health sector perspective to $107,000 per QALY gained. They noted the ICER decreased to $74,000 per added QALY in the societal perspective scenario, which most broadly included patient and caregiver factors.

“Whether the estimates of these caregiver effects used in our modeling are accurate or not, the change in cost-effectiveness results implies that modelers should attempt to perform similar scenario analyses for the modeling of any AD treatment,” Ito and colleagues wrote. “Importantly, federal funding agencies, clinical researchers, life science companies and regulators should require that data on caregiver effects be gathered as core outcome measures in developmental trials of all AD treatments. Better data are needed with which to determine these effects, and ultimately, policy makers and the public should be made aware of the potential importance — and the potential limitations — of analyses using a broader societal perspective when judging the value and fair price for novel AD treatments.”

In a related editorial, Pei-Jung Lin, PhD, and Peter J. Neumann, ScD, both of the Institute for Clinical Research and Health Policy Studies at Tufts Medical Center in Boston, emphasized the importance of improving data collection for AD treatments.

“Ensuring that societal consequences of AD treatments receive attention will require a change in how the field conceptualizes the value of therapies,” Lin and Neumann wrote. “It will also require better data. In particular, data on caregiver costs and health effects should be core outcome measures and ideally collected alongside AD trials, as Ito et al. helpfully point out. Better estimates of how patient and caregiver utilities vary by disease stage and by care setting, among other data inputs, will also help to generate more robust cost-effectiveness estimates.”

Reference:

• Lin P, et al. JAMA Netw Open. 2021;doi:10.1001/jamanetworkopen.2021.31913.