key: cord-1026258-oybxvi2s authors: Lam, Jenny; Jun, Hankyung; Cho, Sang Kyu; Hanson, Mark; Mattke, Soeren title: Projection of budgetary savings to US state Medicaid programs from reduced nursing home use due to an Alzheimer's disease treatment date: 2021-03-17 journal: Alzheimers Dement (Amst) DOI: 10.1002/dad2.12159 sha: 6ba1edf51a81892ba559a3d26c1c1d93e4786ec0 doc_id: 1026258 cord_uid: oybxvi2s INTRODUCTION: The approval of a disease‐modifying Alzheimer's disease (AD) treatment could provide relief to US state budgets that were hit hard by the COVID‐19 pandemic, as mostly Medicare would cover treatment cost, whereas Medicaid would see savings from reduced nursing home use. METHODS: We project savings from 2021 to 2040 with a simulation model from the perspective of state Medicaid programs. RESULTS: Assuming a 40% and 22% relative reduction of disease progression rates with treatment, Medicaid would avoid payments of $186.2 and $93.5 billion for around 1.11 and 0.57 million nursing home patient‐years, respectively. The savings correspond to a 5.06% and 2.49%, respectively, relative reduction of Medicaid spending on nursing home care. Higher per capita savings were projected for older states, those with higher Medicaid payment rates, those with more nursing home residents covered by Medicaid, and those with a lower federal contribution. DISCUSSION: States stand to realize substantial savings from a potential AD treatment. A state's health system preparedness to handle the large number of patients will influence the actual magnitude of the savings and how fast they will accrue. domestic product (GDP) may not reach pre-COVID projected levels until 2030. 6 As states are not permitted to borrow for operational spending, sweeping budget cuts will be inevitable. 1 At the same time, Medicaid enrollment is expected to increase because of the countercyclical nature of the program. Already, Medicaid enrollment and spending have both exceeded pre-pandemic estimates, 7 as approximately 1.7 million additional Americans have enrolled between March 1 and June 1, 2020, in just 26 states. 8 As Medicaid outlays are the single biggest item of state spending, even minor changes to program cost have substantial budget implications. An unexpected source of savings to states may be disease-modifying treatments for Alzheimer's disease (AD), as the Food and Drug Administration (FDA) has accepted an application for aducanumab with June 7, 2021 set as the decision date. 9 The current consensus is that diseasemodifying treatment needs to be started in the mild cognitive impairment (MCI) or mild dementia disease stages with the objective to delay disease progression and allow patients to live independently in their home and community longer. AD being an aging-related disease, the expectation is that longer independence will reduce net nursing home use, as patients may pass away from coexisting conditions while the disease progresses. If approved, the treatment will result in a windfall to states, because the cost of diagnosing and treating patients will largely fall on the Medicare program, as the primary payer of medical care in the elderly and disabled, and to a lesser degree on commercial carriers for patients below age 65. Medicaid programs, on the other hand, only cover medical care for a small subgroup but are the sole payer of last resort for nursing home care for beneficiaries with low income and/or exhausted assets. Thus, they stand to reap net savings from avoided or delayed nursing home admissions because of reduced dementia progression and thus care dependency. 10 The cost of nursing home care to Medicaid programs is substantial. The average annual Medicaid payment rate amounts to $79,588 per Medicaid resident in 2020 dollars with a range from $37,273 in Illinois to $377,310 in Alaska, 11 and long-term care represents ≈22% of expenditures. 12 Approximately 62% of nursing home residents are primarily supported by Medicaid 13 and 57% of the national spending on Long-Term Services and Supports are paid by Medicaid and approximately 79% of expenditures are on nursing homes. 14, 15 Against this background, the objective of this article is to project the potential budgetary savings from a disease-modifying AD treatment due to reduced nursing home use nationally and for each state and the District of Columbia individually. We use a simulation model to estimate the budget impact trajectory from 2021 to 2040. Our simulation model has three components. The first projects the annual number of individuals who will be formally diagnosed with MCI projects how many patients can be diagnosed each year nationally 18 and apply state-specific population numbers by age and sex as well as data for number of specialists and PET scanners to that model. State population projections are based on US Census estimates. 19 The number of dementia specialists in each state were derived from Redi-Data's address lists of the AMA Masterfile. 20 22 adjusted for the increased mortality risk associated with MCI. 27 As patients age in our model, their rates change accordingly. Disease progression, mortality, and nursing home admission rates, as well as the sex-and age-specific hazard ratios used to adjust these baseline rates to model the general US and specific state populations, are presented in Exhibit 1 in supporting information. To evaluate the impact of disease-modifying treatments on nursing home use, we first projected the annual number of patients, who would require nursing home care in the scenario that no treatment is available, using the baseline disease progression rates and risk of nursing home admission by disease stage from literature noted above. We then projected the annual number of patients, who would require nursing home care in the scenario that treatment is available. In this treatment scenario, the diagnosed patients would receive treatment, while undiagnosed patients would not. We modeled treatment effect as a 40% relative reduction of baseline progression rates at the early stages of the disease (i.e., 40% relative reduction in the progression rates from MCI to mild dementia and from mild dementia to moderate dementia) with no effect on progression rates thereafter. Those reductions in disease progression then translated into fewer nursing home admissions. The effect size reflects the effect on a composite measure for Activities of Daily Living (ADCS-ADL-MCI) in the high-dose cohort of the EMERGE trial and expert guidance that changes in this measure is more predictive of nursing home admission than in measures for cognitive decline. 30 We estimated an alternative scenario using the effect size of 22% We conducted a probabilistic sensitivity analysis to reflect the uncertainty in our disease progression model by varying the clinical parameters, that is, the disease progression and mortality rates, the transition rates to nursing home, and the treatment effect. If our sources had provided distributions for the parameters, we used those, otherwise we assumed a range of ±10% with a uniform distribution (Exhibit 1 in the supporting information). We also used a ±10% uniform distribution to vary the treatment effect in the probabilistic sensitivity analysis for both scenarios. We ran 1,000 iterations of the model drawing for each run a parameter value at random from the distribution. To investigate which factors drive state differences in projected savings, we used an ordinary least squares regression model to predict cumulative savings between 2021 and 2040 per capita as a function of a state's proportion of residents aged 65 and older, the share of nursing home residents covered by Medicaid, the Medicaid payment rate for nursing home care in 2020, the 2021 Federal Medical Assistance Percentages, the number of nursing home beds per 100,000 residents, and the number of dementia specialists per 1,000 population in 2020. The analysis was conducted with Stata/MP version 14. As the study does not involve human subjects data, it was exempt from institutional review board evaluation and registration. Without disease-modifying treatment, our model predicts that patient-years in nursing homes. Source: Authors' estimates. The estimates provided here are under the assumption of a 40% relative reduction in disease progression rate with disease-modifying treatment. We project the budget impact of the introduction of a disease- The fact that states stand to make a net financial gain is another example of the distorted incentive structure of the US healthcare system, in which commonly the entity that invests in better outcomes for patients is not the one to realize the cost offsets from better health. 32 The situation would be different, for example, in Germany, which has mandatory long-term care insurance that is administered by the respective public or private payer carrying one's health insurance. With approximately 21% of Medicaid spending going to long-term care, 33 Our study is not without limitations. A simulation does not constitute direct evidence and is fraught with uncertainty over our relatively long horizon. While the probabilistic sensitivity analysis suggests reasonably bounded estimates, unaccounted factors may have a substantial effect on the predictions in either direction. Future changes to the Medicaid program and/or spread of Medicaid waiver programs may increase or decrease the number of nursing home residents covered by Medicaid. Our assumed treatment effect is based on the results from a clinical trial program for a single drug, and it is uncertain how the effect size will generalize to other treatments, to real-world conditions, and to patients with higher comorbidity burden than those in clinical trials, as well as how durable is the effect. However, if history is any indication, future treatments are going to perform better, as we have witnessed for many diseases, such as cancer and HIV/AIDS. We also note that we could not include patients with mild dementia due to AD in the starting cohort, because prevalence data were lacking, although this will have a minor effect, as we do account for patients who progress to mild dementia over the course of the simulation. The actual share of patients seeking evaluation and treatment will depend on various factors, like disease awareness, access to care, the label of a treatment, cost sharing requirements, and receptivity of clinicians. We assume homogeneity of patients, whereas disparities in access to diagnostic services and Medicaid eligibility for nursing home care may change our predictions. Last, we need to emphasize that we only account for savings from reduced nursing home use, while ignoring effects on family caregiver productivity, which are estimated to be the same to twice as high as nursing home cost 37 , and medical cost. To summarize, while we have to acknowledge that our estimates may provide an upper bound, states stand to realize substantial net savings from a potential disease-modifying AD treatment. How fast the savings will accrue and how large they will be is going to depend on how well a state's health system is prepared to handle the large backlog of patients. The study was funded through a research contract from Biogen, Inc. to the University of California. The sponsor provided editorial suggestions on a earlier draft of the manuscript and had no other involvement in the study design, analysis, and decision to submit. We owe our gratitude to Mo Wang and Anissa Dallmann for their help in retrieving, compiling, and deduplicating PET scanner sites. We also want to thank Redi-Data, IMV, and the Intersocietal Accreditation Commission for providing data and reports for this study. The work was funded by a contract from Biogen to the University States Are Broke And Many Are Eyeing Massive Cuts. 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