Predicting sojourn times across dementia disease stages, institutionalization, and mortality

Impact of adult day service on long-term care placement: A scoping review

Cognitive decline and dementia account for the highest number of cognitive disabilities, functional limitations, chronic healthcare conditions, and long-term care needs among older adults aged 65 and older in the US. The optimization of public health policies and advances made from gerontological research have resulted in a steady increase in the number of older adults 65 and above, which makes the risk of cognitive decline and dementia higher. The use of interventions like adult day services (ADS) may delay placement into long-term care homes among older adults living with dementia and other cognitive impairment-related disabilities. The purpose of this scoping review study was to address the research question: What impact do adult day services have on long-term care home placement for people living with dementia? Electronic searches were performed using six databases for sources published between 1998 and May 2024. A total of 150 citations were found. After screening titles and abstracts, full-text reviews were completed for eight articles. Of these eight articles, only two articles addressed the research question directly and reported increased risk for placement. With very few recent studies on the impact of ADS on long-term care placement, more research is needed to draw firm scientific conclusions on the benefit of ADS, and these future studies should include the perspectives of people living with dementia, family caregivers, and ADS providers.

New IPECAD Open-Source Model Framework for the Health Technology Assessment of Early Alzheimer's Disease Treatment: Development and Use Cases

OBJECTIVES

Reimbursement decisions for new Alzheimer's disease (AD) treatments are informed by economic evaluations. An open-source model with intuitive structure for model cross-validation can support the transparency and credibility of such evaluations. We describe the new International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) open-source model framework (version 2) for the health-economic evaluation of early AD treatment and use it for cross-validation and addressing uncertainty.

METHODS

A cohort state-transition model using a categorized composite domain (cognition and function) was developed by replicating an existing reference model and testing it for internal validity. Then, features of existing Institute for Clinical and Economic Review (ICER) and Alzheimer's Disease Archimedes Condition-Event Simulator (AD-ACE) models assessing lecanemab treatment were implemented for model cross-validation. Additional uncertainty scenarios were performed on choice of efficacy outcome from trial, natural disease progression, treatment effect waning and stopping rules, and other methodological choices. The model is available open-source as R code, spreadsheet, and web-based version via https://github.com/ronhandels/IPECAD.

RESULTS

In the IPECAD model incremental life-years, quality-adjusted life-years (QALY) gains and cost savings were 21% to 31% smaller compared with the ICER model and 36% to 56% smaller compared with the AD-ACE model. IPECAD model results were particularly sensitive to assumptions on treatment effect waning and stopping rules and choice of efficacy outcome from trial.

CONCLUSIONS

We demonstrated the ability of a new IPECAD open-source model framework for researchers and decision makers to cross-validate other (Health Technology Assessment submission) models and perform additional uncertainty analyses, setting an example for open science in AD decision modeling and supporting important reimbursement decisions.

IPECAD Modeling Workshop 2023 Cross-Comparison Challenge on Cost-Effectiveness Models in Alzheimer's Disease

OBJECTIVES

Decision-analytic models assessing the value of emerging Alzheimer's disease (AD) treatments are challenged by limited evidence on short-term trial outcomes and uncertainty in extrapolating long-term patient-relevant outcomes. To improve understanding and foster transparency and credibility in modeling methods, we cross-compared AD decision models in a hypothetical context of disease-modifying treatment for mild cognitive impairment (MCI) due to AD.

METHODS

A benchmark scenario (US setting) was used with target population MCI due to AD and a set of synthetically generated hypothetical trial efficacy estimates. Treatment costs were excluded. Model predictions (10-year horizon) were assessed and discussed during a 2-day workshop.

RESULTS

Nine modeling groups provided model predictions. Implementation of treatment effectiveness varied across models based on trial efficacy outcome selection (clinical dementia rating - sum of boxes, clinical dementia rating - global, mini-mental state examination, functional activities questionnaire) and analysis method (observed severity transitions, change from baseline, progression hazard ratio, or calibration to these). Predicted mean time in MCI ranged from 2.6 to 5.2 years for control strategy and from 0.1 to 1.0 years for difference between intervention and control strategies. Predicted quality-adjusted life-year gains ranged from 0.0 to 0.6 and incremental costs (excluding treatment costs) from -US$66 897 to US$11 896.

CONCLUSIONS

Trial data can be implemented in different ways across health-economic models leading to large variation in model predictions. We recommend (1) addressing the choice of outcome measure and treatment effectiveness assumptions in sensitivity analysis, (2) a standardized reporting table for model predictions, and (3) exploring the use of registries for future AD treatments measuring long-term disease progression to reduce uncertainty of extrapolating short-term trial results by health-economic models.

Costs of Care in Relation to Alzheimer's Disease Severity in Sweden: A National Registry-Based Cohort Study

BACKGROUND

The advancement of diagnostic and therapeutic interventions in early Alzheimer's disease (AD) has demanded the economic evaluation of such interventions. Resource utilization and cost estimates in early AD and, more specifically, the amyloid-positive population are still lacking. We aimed to provide cost estimates in AD in relation to disease severity and compare these with the control population. We also aimed to provide cost estimates for a subset of the AD population with both clinical diagnosis and amyloid-positive confirmation.

MATERIALS AND METHODS

This was a retrospective longitudinal analysis of resource utilization using data from national registries. A cohort from the national Swedish registry for cognitive/dementia disorders (SveDem) includes all clinically diagnosed AD between 2013 and 2020. The study population included 31,951 people with AD and 63,902 age- and sex-matched controls (1:2). The population was followed until death, the end of December 2020, or 2 years from the last clinic visit. Direct medical and social costs were estimated from other national registries. Direct medical costs include costs for medications and inpatient and outpatient clinical visits. Direct social costs include costs for institutionalization, home care, short-term care, support for daytime activities, and housing support. Mean annual costs and 95% confidence intervals were obtained by bootstrapping, presented in 2021 Swedish Krona (SEK) (1 SEK = 0.117 USD, 1 SEK = 0.0985 EUR in 2021), and disaggregated by AD severity, cost component, sex, age group, and care setting.

RESULTS

Mean annual costs for individuals with clinically diagnosed AD were SEK 99,906, SEK 290,972, SEK 479,524, and SEK 795,617 in mild cognitive impairment (MCI), mild, moderate, and severe AD. The mean annual costs for the population with both clinical diagnosis and amyloid-positive AD confirmation (N = 5610) were SEK 57,625, SEK 179,153, SEK 333,095, and SEK 668,073 in MCI, mild, moderate, and severe AD, respectively. The mean annual costs were higher in institutionalized than non-institutionalized patients, females than males, and older than younger age groups. Inpatient and drug costs were similar in all AD severity stages, but outpatient costs decreased with AD severity. Costs for institutionalization, home care, support for daytime activities, and short-term care increased with AD severity, whereas the cost of housing support decreased with AD severity.

CONCLUSIONS

This is the first study estimating annual costs in people with AD from MCI to severe AD, including those for the amyloid-positive population. The study provides cost estimates by AD severity, cost components, care settings, sex, and age groups, allowing health economic modelers to apply the costs based on different model structures and populations.

Pathways to personalized medicine-Embracing heterogeneity for progress in clinical therapeutics research in Alzheimer's disease

Biological and clinical heterogeneity is a major challenge in research for developing new treatments for Alzheimer's disease (AD). AD may be defined by its amyloid beta and tau pathologies, but we recognize that mixed pathologies are common, and that diverse genetics, central nervous system (CNS) and systemic pathophysiological processes, and environmental/experiential factors contribute to AD's diverse clinical and neuropathological features. All these factors are rational targets for therapeutic development; indeed, there are hundreds of candidate pharmacological, dietary, neurostimulation, and lifestyle interventions that show benefits in homogeneous laboratory models. Conventional clinical trial designs accommodate heterogeneity poorly, and this may be one reason that progress in translating candidate interventions has been so difficult. We review the challenges of AD's heterogeneity for the clinical trials enterprise. We then discuss how advances in repeatable biomarkers and digital phenotyping enable novel "single-case" and adaptive trial designs to accelerate therapeutics development, moving us closer to personalized research and medicine for AD. HIGHLIGHTS: Alzheimer's disease is diverse in its clinical features, course, risks, and biology. Typical randomized controlled trials are exclusive and necessarily large to attain arm comparability with broad outcomes. Repeated blood biomarkers and digital tracking can improve outcome measure precision and sensitivity. This enables the use of novel "single-case" and adaptive trial designs for inclusivity, rigor, and efficiency.