Cost-effectiveness of cerebrospinal biomarkers for the diagnosis of Alzheimer’s disease

Background: Accurate and timely diagnosis of Alzheimer’s disease (AD) is important for prompt initiation of treatment in patients with AD and to avoid inappropriate treatment of patients with false-positive diagnoses. Methods: Using a Markov model, we estimated the lifetime costs and quality-adjusted life-years (QALYs) of cerebrospinal fluid biomarker analysis in a cohort of patients referred to a neurologist or memory clinic with suspected AD who remained without a definitive diagnosis of AD or another condition after neuroimaging. Parametric values were estimated from previous health economic models and the medical literature. Extensive deterministic and probabilistic sensitivity analyses were performed to evaluate the robustness of the results. Results: At a 12.7% pretest probability of AD, biomarker analysis after normal neuroimaging findings has an incremental cost-effectiveness ratio (ICER) of $11,032 per QALY gained. Results were sensitive to the pretest prevalence of AD, and the ICER increased to over$50,000 per QALY when the prevalence of AD fell below 9%. Results were also sensitive to patient age (biomarkers are less cost-effective in older cohorts), treatment uptake and adherence, biomarker test characteristics, and the degree to which patients with suspected AD who do not have AD benefit from AD treatment when they are falsely diagnosed. Conclusions: The cost-effectiveness of biomarker analysis depends critically on the prevalence of AD in the tested population. In general practice, where the prevalence of AD after clinical assessment and normal neuroimaging findings may be low, biomarker analysis is unlikely to be cost-effective at a willingness-to-pay threshold of $50,000 per QALY gained. However, when at least 1 in 11 patients has AD after normal neuroimaging findings, biomarker analysis is likely cost-effective. Specifically, for patients referred to memory clinics with memory impairment who do not present neuroimaging evidence of medial temporal lobe atrophy, pretest prevalence of AD may exceed 15%. Biomarker analysis is a potentially cost-saving diagnostic method and should be considered for adoption in high-prevalence centers

Predicting Joint Replacement Waiting Times

Currently, the median waiting time for total hip and knee replacement in Ontario is greater than 6 months. Waiting longer than 6 months is not recommended and may result in lower post-operative benefits. We developed a simulation model to estimate the proportion of patients who would receive surgery within the recommended waiting time for surgery over a 10-year period considering a wide range of demand projections and varying the number of available surgeries. Using an estimate that demand will grow by approximately 8.7% each year for 10 years, we determined that increasing available supply by 10% each year was unable to maintain the status quo for 10 years. Reducing waiting times within 10 years required that the annual supply of surgeries increased by 12% or greater. Allocating surgeries across regions in proportion to each region’s waiting time resulted in a more efficient distribution of surgeries and a greater reduction in waiting times in the long-term compared to allocation strategies based only on the region’s population size

An Evaluation of Strategies to Reduce Waiting Times for Total Joint Replacement in Ontario

Background: In 2005, the median waiting time for total hip and knee joint replacements in Ontario was greater than 6 months, which is considered longer than clinically appropriate. Demand is expected to increase and exacerbate already long waiting times. Solutions are needed to reduce waiting times and improve waiting list management. Methods: We developed a discrete event simulation model of the Ontario total joint replacement system to evaluate the effects of 4 management strategies on waiting times: (1) reductions in surgical demand; (2) formal clinical prioritization; (3) waiting time guarantees; and (4) common waiting list management. Results: If the number of surgeries performed increases by less than 10% each year, then demand must be reduced by at least 15% to ensure that, within 10 years, 90% of patients receive surgery within their maximum recommended waiting time. Clinically prioritizing patients reduced waiting times for high-priority patients and increased the number of patients at all priority levels who received surgery each year within recommended maximum waiting times by 9.3%. A waiting time guarantee for all patients provided fewer surgeries within recommended waiting times. Common waiting list management improved efficiency and increased equity in waiting across regions. Discussion: Dramatically increasing the supply of joint replacement surgeries or diverting demand for surgeries to other jurisdictions will reduce waiting times for total joint replacement surgery. Introducing a strictly adhered to patient prioritization scheme will ensure that more patients receive surgery within severity-specific waiting time targets. Implementing a waiting time guarantee for all patients will not reduce waiting times—it will only shuffle waiting times from some patients to others. To reduce waiting times to clinically acceptable levels within 10 years, increases in the number of surgeries provided greater than those observed historically or reductions in demand are needed

Cost-effectiveness of cerebrospinal biomarkers for the diagnosis of Alzheimer\u27s disease

Background: Accurate and timely diagnosis of Alzheimer\u27s disease (AD) is important for prompt initiation of treatment in patients with AD and to avoid inappropriate treatment of patients with false-positive diagnoses. Methods: Using a Markov model, we estimated the lifetime costs and quality-adjusted life-years (QALYs) of cerebrospinal fluid biomarker analysis in a cohort of patients referred to a neurologist or memory clinic with suspected AD who remained without a definitive diagnosis of AD or another condition after neuroimaging. Parametric values were estimated from previous health economic models and the medical literature. Extensive deterministic and probabilistic sensitivity analyses were performed to evaluate the robustness of the results. Results: At a 12.7% pretest probability of AD, biomarker analysis after normal neuroimaging findings has an incremental cost-effectiveness ratio (ICER) of $11,032 per QALY gained. Results were sensitive to the pretest prevalence of AD, and the ICER increased to over$ 50,000 per QALY when the prevalence of AD fell below 9%. Results were also sensitive to patient age (biomarkers are less cost-effective in older cohorts), treatment uptake and adherence, biomarker test characteristics, and the degree to which patients with suspected AD who do not have AD benefit from AD treatment when they are falsely diagnosed. Conclusions: The cost-effectiveness of biomarker analysis depends critically on the prevalence of AD in the tested population. In general practice, where the prevalence of AD after clinical assessment and normal neuroimaging findings may be low, biomarker analysis is unlikely to be cost-effective at a willingness-to-pay threshold of $ 50,000 per QALY gained. However, when at least 1 in 11 patients has AD after normal neuroimaging findings, biomarker analysis is likely cost-effective. Specifically, for patients referred to memory clinics with memory impairment who do not present neuroimaging evidence of medial temporal lobe atrophy, pretest prevalence of AD may exceed 15%. Biomarker analysis is a potentially cost-saving diagnostic method and should be considered for adoption in high-prevalence centers

Lung cancer treatment costs, including patient responsibility, by disease stage and treatment modality, 1992 to 2003

AbstractObjectivesThe objective of this analysis was to estimate costs for lung cancer care and evaluate trends in the share of treatment costs that are the responsibility of Medicare beneficiaries.MethodsThe Surveillance, Epidemiology, and End Results (SEER)-Medicare data from 1991–2003 for 60,231 patients with lung cancer were used to estimate monthly and patient-liability costs for clinical phases of lung cancer (prediagnosis, staging, initial, continuing, and terminal), stratified by treatment, stage, and non-small- versus small-cell lung cancer. Lung cancer-attributable costs were estimated by subtracting each patient's own prediagnosis costs. Costs were estimated as the sum of Medicare reimbursements (payments from Medicare to the service provider), co-insurance reimbursements, and patient-liability costs (deductibles and “co-payments” that are the patient's responsibility). Costs and patient-liability costs were fit with regression models to compare trends by calendar year, adjusting for age at diagnosis.ResultsThe monthly treatment costs for a 72-year-old patient, diagnosed with lung cancer in 2000, in the first 6 months ranged from $2687 (no active treatment) to$9360 (chemo-radiotherapy); costs varied by stage at diagnosis and histologic type. Patient liability represented up to 21.6% of care costs and increased over the period 1992–2003 for most stage and treatment categories, even when care costs decreased or remained unchanged. The greatest monthly patient liability was incurred by chemo-radiotherapy patients, which ranged from $1617 to$2004 per month across cancer stages.ConclusionsCosts for lung cancer care are substantial, and Medicare is paying a smaller proportion of the total cost over time

Milder Alzheimer\u27s Disease Pathology in Heart Failure and Atrial Fibrillation

Introduction:Heart failure (HF) and atrial fibrillation (AF) have been associated with a higher risk of Alzheimer’s disease (AD). Whether HF and AF are related to AD by enhancing AD neuropathological changes is unknown. Methods:We applied network analyses and multiple logistic regression models to assess the association between HF and AF with severity of AD neuropathology in patients from the National Alzheimer’s Coordinating Center database with primary neuropathological diagnosis of AD. Results:We included 1593 patients, of whom 129 had HF and 250 had AF. HF and AF patients were older and had milder AD pathology. In the network analyses, HF and AF were associated with milder AD neuropathology. In the regression analyses, age (odds ratio [OR] 0.94, 95

A comparison of COVID-19 epidemiological indicators in Sweden, Norway, Denmark, and Finland

Aims: To compare the early impact of COVID-19 infections and mortality from February to July 2020 across the Nordic nations of Sweden, Norway, Denmark, and Finland through available public data sources and conduct a descriptive analysis of the potential factors that drove different epidemiological outcomes, with a focus on Sweden’s response. Methods: COVID-19 cases, deaths, tests, case age distribution, and the difference between 2020 all-cause mortality and the average mortality of the previous 5 years were compared across nations. Patterns in cell phone mobility data, testing strategies, and seniors’ care home deaths were also compared. Data for each nation were based on publicly available sources as of July 31, 2020. Results: Compared with its Nordic peers, Sweden had a higher incidence rate across all ages, a higher COVID-19-related death rate only partially explained by population demographics, a higher death rate in seniors’ care, and higher all-cause mortality. Sweden had approximately half as much mobility change as its Nordic neighbours until April and followed similar rates as its neighbours from April to July. Denmark led its Nordic peers in testing rates, while Sweden had the highest cumulative test-positivity rate continuously from mid-March. Conclusions: COVID-19 pushed Sweden’s health system to its capacity, exposed systemic weaknesses in the seniors’ care system, and revealed challenges with implementing effective contact tracing and testing strategies while experiencing a high case burden. Looser government restrictions at the beginning of the outbreak are likely to have played a role in the impact of COVID-19 in Sweden. In an effort to improve epidemic control, Sweden has increased testing rates, implemented more restrictive prevention measures, and increased their intensive care unit bed capacity

Optimal Information Collection Policies in a Markov Decision Process Framework

The cost-effectiveness and value of additional information about a health technology or program may change over time because of trends affecting patient cohorts and/or the intervention. Delaying information collection even for parameters that do not change over time may be optimal. Methods. We present a stochastic dynamic programming approach to simultaneously identify the optimal intervention and information collection policies. We use our framework to evaluate birth cohort hepatitis C virus (HCV) screening. We focus on how the presence of a time-varying parameter (HCV prevalence) affects the optimal information collection policy for a parameter assumed constant across birth cohorts: liver fibrosis stage distribution for screen-detected diagnosis at age 50. Results. We prove that it may be optimal to delay information collection until a time when the information more immediately affects decision making. For the example of HCV screening, given initial beliefs, the optimal policy (at 2010) was to continue screening and collect information about the distribution of liver fibrosis at screen-detected diagnosis in 12 years, increasing the expected incremental net monetary benefit (INMB) by $169.5 million compared to current guidelines. Conclusions. The option to delay information collection until the information is sufficiently likely to influence decisions can increase efficiency. A dynamic programming framework enables an assessment of the marginal value of information and determines the optimal policy, including when and how much information to collect