Abstract Details

Title Estimates of Productivity Loss Due to Neurological Diseases in the United States

Topic General Neurology

Presentation(s) P7 - Poster Session 7 (11:45 AM-12:45 PM)

Poster/Presentation
Number 4-007

Objective To estimate the impact of five neurological diseases (NDs)—Alzheimer’s disease (AD), relapsing-remitting multiple sclerosis (RRMS), secondary progressive MS (SPMS), Parkinson’s disease (PD) and generalized myasthenia gravis (gMG)—on patients’ ability to be productive in the formal workforce (market productivity) and in societal contributions outside the formal workforce (non-market productivity).

Background While it is recognized that productivity of people living with NDs is negatively impacted, data on the overall burden of productivity loss are limited. More robust information on the burden of these diseases and the impact of treatment is needed to support more informed decision-making about value assessment, access and reimbursement.

Design/Methods We combined disease-related quality of life with a nationally representative predictive algorithm (Jiao et al. 2023) to estimate market and non-market productivity by disease and disease severity. We valued time equally across market and non-market productivity regardless of sex, age or condition at the average marginal pre-tax wage plus fringe benefits.

Results Ranges of annual estimated productivity loss per patient across disease severity were $10,729 (mild cognitive impairment) to $53,828 (moderate/severe) for AD; $14,935 (no/minimal disability) to $89,738 (bedridden) for RRMS; $20,415 (no/minimal disability) to $97,591 (bedridden) for SPMS; $981 (Hoehn and Yahr stage I) to $72,404 (Hoehn and Yahr stage IV) for PD; and $2521 (class I) to $32,354 (class IV) for gMG. On average, market (vs non-market) productivity loss accounted for ≈60% of the total loss.

Conclusions The cost of productivity loss per patient was substantial and increased with severity across all indications. A total burden of disease estimate is fundamentally incomplete without consideration of patients’ perspectives and experiences, including how these experiences shift as diseases progress. Our findings can support improved decision-making by incorporating productivity loss in how we value treatments and also highlight the need for disease-specific and patient-centered data collection.

Authors/Disclosures
Nikki Win PRESENTER	Nikki Win has received personal compensation for serving as an employee of Genentech. Nikki Win has stock in Genentech .
Julia Fox (Genentech, University of Washington)	No disclosure on file
Elizabeth Mearns, PhD (Genentech)	Dr. Mearns has received personal compensation for serving as an employee of Genentech. Dr. Mearns has stock in Genentech.
Thomas Majda (Genentech)	No disclosure on file
Katherine Rosettie (Genentech)	No disclosure on file
Stacey Kowal (Genentech)	No disclosure on file

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