Health Outcomes Research of Novel Disease Modifying Medications in Alzheimer’s Disease and Cost Burden of Early Onset Dementia

The main etiologies of dementia, a neurodegenerative disease, consist of: Alzheimer’s Disease (AD), Vascular Dementia (VD), Frontotemporal Lobar Dementia (FTD), and Lewy Body Dementia (LBD). AD the most common form of dementia is the sixth leading cause of death in the US, where currently 5.3 million Americans are diagnosed with Late-Onset and 95% of cases are 65 years and older. Early-Onset represents the remaining 5% of cases where ages at diagnosis is younger than 65 years. AD is characterized by a progressive loss of neurons with impact on patient cognition, function, and behavior. The 2015 Alzheimer’s Association Report estimated direct and indirect costs of AD and other dementias will reach $226 billion with an expected five-fold increase to $1.1 trillion by the year 2050. With no treatment available that stops, or slows down progression of the disease places the cost estimates of AD and dementia among the most expensive chronic diseases. The next generation of AD medications being investigated will target progression of the disease. Disease-modifying medications (DMMs) are being developed with a mechanism of action directed towards the main hallmarks found in AD patients: the amyloid-beta (Aβ) plaques, and the tau tangles. Tolfenamic acid, a non-steroidal anti-inflammatory (NSAID) drug, is being repurposed in the US as a DMM for AD treatment; human clinical trials still pending. Aducanumab, a monoclonal antibody, binds Aβ and increases its clearance; Phase III human clinical trials are in progress. DMMs are anticipated to improve cognition, function and behavior. The objectives, hypotheses, methods and results of this dissertation follow the manuscript format, and are three fold: Manuscript 1: The objective was to estimate cost-effectiveness of novel disease-modifying medication (DMM) compared to standard medication currently used in the treatment of Alzheimer’s disease. The hypothesis was that the DMM option will show a favorable cost-effectiveness when compared to standard care. Using a Markov Model with a study population comprised of a hypothetical 1000 patients, 65 years and older, we evaluated quality life years (QALYs) gained by the new DMM and an appropriate price to develop a cost-effectiveness framework for the new product. In the Markov model we were able to determine an increase in QALYs when compared to standard of care with a cost value for DMM much higher than current standard care while still showing cost-effectiveness as a new treatment option. Manuscript 2: The objective was to determine affordability to payer’s budget i.e. insurance or hospital upon the introduction of the new cost-effective diseasemodifying medication (DMM) class in treatment of Alzheimer’s disease. The hypothesis was that the introduction of DMM will have minimal budgetary changes to direct costs incurred by payers. Using a 1-year budget impact analysis, a prospective short-term analysis was conducted using Optum ClinformaticsTM Data Mart (January 2010-Decemeber 2012), a large national insurer database with administrative health claims information, with a study population of patients 65 years and older. Two scenarios are to be compared: current mix treatment costs of medications used in Alzheimer’s versus a new mix treatment cost that included the addition of DMM to current mix treatment. The difference in total payer cost of the two scenarios represents the budget impact of the new therapy implementation, allowing us to predicate future cost of new treatment mix. The study estimated a total per-memberper-month (PMPM) treatment cost preand postintroduction of DMM that would be affordable to payer’s and recommended to be added to formulary. Manuscript 3: The objective was to describe prevalence, incidence, and direct total cost predictors associated with Early-Onset Dementia (EOD) and its etiologies. The hypothesis was that Alzheimer’s disease would be main predicator of overall EOD direct cost. We conducted a retrospective cohort study using Optum ClinformaticsTM Data Mart (January 2010-Decemeber 2012), a large national insurer database with administrative health claims information, with a study population of patients 21-64 years and older. Total cost components include: physician visits, hospital visits, nursing home care, and prescription drugs associated with EOD treatment. Using a Generalized Linear Model (GLM) to assess the relationships between total cost and the covariates of interest, we identified age, geographical regions, EOD subtypes, and comorbidities as total cost predictors of EOD.