Modeling screening, prevention, and delaying of Alzheimer's disease: an early-stage decision analytic model

<p>Abstract</p> <p>Background</p> <p>Alzheimer's Disease (AD) affects a growing proportion of the population each year. Novel therapies on the horizon may slow the progress of AD symptoms and avoid cases altogether. Initiating treatment for the underlying pathology of AD would ideally be based on biomarker screening tools identifying pre-symptomatic individuals. Early-stage modeling provides estimates of potential outcomes and informs policy development.</p> <p>Methods</p> <p>A time-to-event (TTE) simulation provided estimates of screening asymptomatic patients in the general population age ≥55 and treatment impact on the number of patients reaching AD. Patients were followed from AD screen until all-cause death. Baseline sensitivity and specificity were 0.87 and 0.78, with treatment on positive screen. Treatment slowed progression by 50%. Events were scheduled using literature-based age-dependent incidences of AD and death.</p> <p>Results</p> <p>The base case results indicated increased AD free years (AD-FYs) through delays in onset and a reduction of 20 AD cases per 1000 screened individuals. Patients completely avoiding AD accounted for 61% of the incremental AD-FYs gained. Total years of treatment per 1000 screened patients was 2,611. The number-needed-to-screen was 51 and the number-needed-to-treat was 12 to avoid one case of AD. One-way sensitivity analysis indicated that duration of screening sensitivity and rescreen interval impact AD-FYs the most. A two-way sensitivity analysis found that for a test with an extended duration of sensitivity (15 years) the number of AD cases avoided was 6,000-7,000 cases for a test with higher sensitivity and specificity (0.90,0.90).</p> <p>Conclusions</p> <p>This study yielded valuable parameter range estimates at an early stage in the study of screening for AD. Analysis identified duration of screening sensitivity as a key variable that may be unavailable from clinical trials.</p

Patient Pathway Modelling Using Discrete Event Simulation to Improve the Management of COPD

This is an Accepted Manuscript version of 'Usame Yakutcan, Eren Demir, John R. Hurst & Paul C. Taylor (2020) Patient pathway modelling using discrete event simulation to improve the management of COPD, Journal of the Operational Research Society, DOI: 10.1080/01605682.2020.1854626'. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.” Publisher Copyright: © Operational Research Society 2020.The number of people affected by chronic obstructive pulmonary disease (COPD) is increasing and the hospital readmission rate is remarkably high. Therefore, healthcare professionals and managers have financial and workforce-related pressures. A decision support toolkit (DST) for improving the management and efficiency of COPD care is needed to respond to the needs of patients now and in the future. In collaboration with the COPD team of a hospital and community service in London, we conceptualised the pathway for COPD patients and developed a discrete event simulation model (DES) incorporating the dynamics of patient readmissions. A DES model or operational model at this scale has never been previously developed, despite many studies using other modelling and simulation techniques in COPD. Our model is the first of its kind to include COPD readmissions as well as assessing the quantifiable impact of re-designing COPD services. We demonstrate the impact of post-exacerbation pulmonary rehabilitation (PEPR) policy and observe that PEPR would be cost-effective with improvements in quality-adjusted life years (QALYs), reduction in emergency readmissions and occupied bed days. The DST improves the understanding of the impact of scenarios (activities, resources, financial implications etc.) for key decision makers and supports commissioners in implementing the interventions.Peer reviewedFinal Accepted Versio

BROADENING THE PERSPECTIVE OF ECONOMIC EVALUATION IN HEALTH CARE – A CASE STUDY IN DEMENTIA CARE IN THE UK

My thesis is an investigation of the methods to implement a broader societal perspective in economic evaluation. I proposed two potential approaches that could be used to implement a societal perspective in economic evaluation: An extended cost-per-QALY approach and a CCA-MCDA approach. The investigation was conducted in a case study of dementia care. The case study concerned the evaluation of 4 mutually exclusive options for primary care to early detect people with dementia. I reviewed previous economic evaluation studies in dementia and developed a new cost-effectiveness model for this evaluation. The model provided estimates for costs including health care, government-funded social care, privately funded social care and informal care. Benefits were measured in terms of patient QALYs and carer QALYs. Using the model’s estimates as the initial basis, I applied the principles of the extended cost-per-QALY approach and the CCA-MCDA to implement a broader societal perspective in the economic evaluation in the case study

An economic analysis of the quality of primary care for the management of comorbidities in patients living with dementia

The thesis examines the quality of primary care services across a range of comorbid conditions in patients with dementia. The key aims were to assess whether their dementia diagnoses may hinder access to high quality care compared to patients without a cognitive impairment older adults, the health implications of this, and whether quality could be modified. An economic framework is proposed, suggesting that the quality of care received is a function of the supply and demand for quality. Patients with dementia may have a diminishing demand for quality due to a decreased comprehension of their health status as cognitive function declines. On the supply side, quality care provided by physicians may be a function of the availability of resources and the motivation to provide high quality care, which could be financial or not. A systematic literature review and meta-analysis was conducted and highlighted that a diagnosis of dementia is associated with not meeting quality indicators for a range of non-dementia conditions. Subsequent analysis on the English Longitudinal Survey of Ageing, including care quality indicators specifically selected for UK older adults, supports these findings in suggesting that quality is unequal between patients with dementia and patients without cognitive impairments. Further analysis showed that meeting some of these indicators was associated with improved survival and could reduce social care use in patients with dementia, implying that care quality should be improved. In order to assess how care quality and the consequential health outcomes could be improved for patients with dementia, later analyses in this thesis aimed to identify interventions or policies that could improve care quality. Pay-for-performance measures (the Quality and Outcomes Framework) as well as higher levels of cognitive function in patients with dementia appear to be associated with higher quality care. I developed an early model to evaluate the potential cost-effectiveness of introducing a cognition and independence promoting intervention or expanding the QOF to provide additional incentives for patients with dementia. Expanding the QOF does not appear to be cost-effective compared to current practices, though there may be some benefit in promoting cognition and independence in patients with dementia. However, further research on the valuation of health in patients with dementia is required to validate these findings within current willingness-to-pay frameworks for healthcare. The findings of this thesis show that there are some inequalities in the delivery of high-quality primary care, to the detriment of health of patients with dementia. It is implicated that there may be economically efficient strategies to improve health outcomes for patients with dementia by promoting independence

Modelling Health and Healthcare for an Ageing Population

Population ageing has received much attention as a contributing cause of spiralling healthcare expenditure. This study primarily aims to estimate the impact of population ageing on key diseases, and to develop a flexible modelling framework that can inform policy decisions. This research provides a proof-of-concept model where individual Discrete Event Simulation models for three diseases (heart disease, Alzheimer’s disease, and osteoporosis) were extended from existing published models to simulate the general UK population aged 45 years and older, and combined within a single model. Using external population projection data incorporating potential demographic changes, the methods for projecting future healthcare expenditures for the three diseases were demonstrated and the relative benefits of improving treatment of each of the diseases evaluated. Secondary outcomes include the development of a pragmatic literature search method which can be used for literature within diffuse topic areas, and a literature repository for future researchers to explore the existing literature on ageing and healthcare expenditure. Expenditure for the three diseases is projected to increase from £16 billion in 2012 to £28 billion in 2037. A key finding from this work is that the estimates of costs, quality-adjusted life years (QALYs), and the projected expenditure for healthcare services can differ when multiple diseases are modelled in a single model compared with the summed results from single disease models. This implies that policy decisions on the allocation and planning of healthcare resources based on the results from individual disease models can be different from those based on linked models. The novel approach of linking multiple disease models with correlations incorporated provides a new methodological option primarily for modellers who undertake research on comorbidities. It also has potential for wider applications in informing decisions on commissioning of healthcare services and long-term priority setting across diseases and healthcare programmes, hence ultimately contributing to the improvement of population health

Annual Report 2014

This document is a compendium of the research activity carried out by the IR3C during the year 2014, and describes its eleven Research Groups of Excellence, the two lines of research, the publications, funded projects, patents, and other activities within this period

Cognitive impairment and service use: The relationship between research and policy

This thesis examines the association between healthcare service use and cognitive functioning in individuals aged 60 years and over. It examines the association between use of primary and secondary health care services, specifically general practitioners and hospitals, and cognitive impairment. This thesis uses secondary data from the ANU Personality and Total Health (PATH) Through life study. PATH is a longitudinal health study which examines three age-cohorts residing in the Australian Capital Territory and surrounding regions over 12 years. The PATH study also has a number of sub-studies, one of which is the Health and memory sub-study. This sub-study identifies individuals in the PATH sample who would be clinically classified as having mild cognitive disorder (MCD) or dementia. Data on health service use has been obtained by linking three administrative datasets to PATH. Data on primary health care usage was obtained from the Medicare Benefits Schedule. Data on secondary health care usage was obtained from the ACT Admitted Patient Care dataset and the ACT Emergency Department Information. From this linkage, we have information on number of general practitioner visits over a year, number of hospital admissions, length of hospital stay and number of emergency department presentations for each consenting participant. Analysis of general practitioners focused on the impact cognitive impairment had on use over the 12 years of study. Using negative binomial models this analysis found that individuals with MCD visited their general practitioner significantly more than individuals who were cognitively healthy. This use almost doubled when individuals had a comorbid condition of depression or arthritis. Analysis relating to hospitalisation also focused on the association between use and cognitive impairment longitudinally. This analysis found that individuals who were hospitalised had significant declines in particular cognitive tests compared to individuals who were not hospitalised. This thesis also examined factors which impacted on general practitioner, hospital and emergency department use. Predictors of use were examined for individuals with MCD or dementia compared with cognitively healthy individuals, based on the Andersen-Newman model of health behaviour. Analysis using logistic regression models found that individuals with MCD and dementia had higher usage of all three services compared to cognitively healthy individuals. This study also found that need variables were the strongest predictor of healthcare service use. However, the types of predisposing, enabling and need variables varied depending on the healthcare service (general practitioner, same day hospital, multiple day hospital or emergency department) and whether the individual had MCD, dementia or was cognitively healthy. The information and findings relating to cognitive impairment and health service use are important for policy and practice. Communication of research to policy makers for the development of policy, termed knowledge translation, is discussed in the thesis. Several important models of knowledge translation are outlined and there is a discussion about how to strengthen the relationship between researchers and policy makers. The thesis concludes with a discussion on future policies and practices to increase early detection and diagnosis of MCD and dementia through prevention and screening in healthcare services

Machine learning techniques implementation in power optimization, data processing, and bio-medical applications

The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for demand side management of electric water heaters using Q-learning and action-dependent heuristic dynamic programming. The implemented approaches provide an efficient load management mechanism that reduces the overall power cost and smooths grid load profile. The second paper implements an ensemble statistical and subspace-clustering model for analyzing the heterogeneous data of the autism spectrum disorder. The paper implements a novel k-dimensional algorithm that shows efficiency in handling heterogeneous dataset. The third paper provides a unified learning model for clustering neuroimaging data to identify the potential risk factors for suboptimal brain aging. In the last paper, clustering and clustering validation indices are utilized to identify the groups of compounds that are responsible for plant uptake and contaminant transportation from roots to plants edible parts --Abstract, page iv