Estimation of Utility Values for Computing Quality-adjusted Life Years Associated With Homelessness

Medical Care ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Suja S. Rajan ◽  
Jack Tsai
Keyword(s):  
Quality Adjusted Life Years ◽  
Utility Values ◽  
Life Years ◽  
Quality Adjusted Life

scholarly journals Evaluating Quality Adjusted Life Years in the Absence of Standard Utility Values- A Dynamic Joint Modelling Approach

2020 ◽  
Author(s):  
Vishal Deo ◽  
Gurprit Grover
Keyword(s):  
Joint Model ◽  
Monte Carlo Integration ◽  
Survival Times ◽  
Quality Adjusted Life Years ◽  
Joint Modelling ◽  
Utility Values ◽  
Decision Analytic Modelling ◽  
Life Years ◽  
Quality Adjusted Life ◽  
Modelling Approach

AbstractEstimation of Quality Adjusted Life Years (QALYs) is pivotal towards cost-effectiveness analysis (CEA) of medical interventions. Most of the CEA studies employ multi-state decision analytic modelling approach, where fixed utility values are assigned to each disease state and total QALYs are calculated on the basis of total lengths of stay in each state.In this paper, we have formulated a new approach to CEA by defining utility as a function of a longitudinal covariate which is significantly associated with disease progression. Association parameter between the longitudinal covariate and survival times is estimated through joint modelling of the longitudinal linear mixed effects model and the Weibull accelerated failure time survival model. Metropolis-Hastings algorithm and Monte Carlo integration are used to predict expected survival times of each censored case using the joint model. Fitted longitudinal model is further used to project values of the longitudinal covariate at all time points for each patient. Utility values calculated using these projected covariate values are used to evaluate QALYs for each patient.Retrospective survival data of HIV/ AIDS patients undergoing treatment at the Antiretroviral Therapy centre of Ram Manohar Lohia hospital in New Delhi is used to demonstrate the implementation of the proposed methodology. A simulation exercise is also carried out to gauge the predictive capability of the joint model in projecting the values of the longitudinal covariate.The proposed dynamic approach to calculate QALY provides a promising alternative to the popular multi-state decision analytic modelling approach, especially when the standard utility values for different stages of the concerned disease are not available.

Identifying Public Health Problems

2018 ◽  
Author(s):  
Scott Burris ◽  
Micah L. Berman ◽  
Matthew Penn, and ◽  
Tara Ramanathan Holiday
Keyword(s):  
Public Health ◽  
Health Problems ◽  
Health Priorities ◽  
Disability Adjusted Life Years ◽  
Quality Adjusted Life Years ◽  
Disability Adjusted Life ◽  
Life Years ◽  
Study Designs ◽  
Quality Adjusted Life

Chapter 5 discusses the use of epidemiology to identify the source of public health problems and inform policymaking. It uses a case study to illustrate how researchers, policymakers, and practitioners detect diseases, identify their sources, determine the extent of an outbreak, and prevent new infections. The chapter also defines key measures in epidemiology that can indicate public health priorities, including morbidity and mortality, years of potential life lost, and measures of lifetime impacts, including disability-adjusted life years and quality-adjusted life years. Finally, the chapter reviews epidemiological study designs, differentiating between experimental and observational studies, to show how to interpret data and identify limitations.

Medicare’s Hospital Value-Based Purchasing Program Values Quality over QALYs

2021 ◽  
pp. 0272989X2110171
Author(s):  
Edward C. Norton ◽  
Jun Li ◽  
Anup Das ◽  
Andrew M. Ryan ◽  
Lena M. Chen
Keyword(s):  
Quality Of Care ◽  
Pay For Performance ◽  
Health Interventions ◽  
Quality Adjusted Life Years ◽  
Value Based Purchasing ◽  
Quality Performance ◽  
Life Years ◽  
Policy Adjustments ◽  
Quality Adjusted Life

Medicare’s Hospital Value-Based Purchasing Program (HVBP) is the first national pay-for-performance program to combine measures of quality of care with a measure of episode spending. We estimated the implicit tradeoffs between mortality reduction and spending reduction. To earn points in HVBP, a hospital can either lower mortality or reduce spending, creating a tradeoff between the 2 measures. We analyzed the quality performance and earned points of 2814 hospitals using publicly available data. We then quantified the tradeoffs between spending and mortality in terms of quality-adjusted life-years (QALYs). If incentives in the program were balanced, then the tradeoff between spending and QALYs should be comparable with those of high-value health interventions, roughly $50,000 to $200,000 per QALY. Instead, the tradeoff in HVBP was about $1.2 million per QALY. HVBP overvalues improvements in quality of care relative to spending reductions. We propose 2 possible policy adjustments that could improve incentives for hospitals to deliver high-value care.

Good, Fairness and QALYs

1988 ◽  
Vol 23 ◽  
pp. 57-73 ◽  
Author(s):  
John Broome
Keyword(s):  
Health Service ◽  
Quality Adjusted Life Years ◽  
Life Years ◽  
Quality Adjusted Life

Counting QALYs (quality adjusted life years) has been proposed as a way of deciding how resources should be distributed in the health service: put resources where they will produce the most QALYs. This proposal has encountered strong opposition. There has been a disagreement between some economists favouring QALYs and some philosophers opposing them. But the argument has, I think, mostly been at cross-purposes. Those in favour of QALYs point out what they can do, and those against point out what they can't. There need be no disagreement about this. What is needed is to sort out what is the proper domain of QALYs, and it may be possible to do this amicably. Then we may be able to get on with the more useful job of deciding how well QALYs perform within their domain. In this paper I shall try to accomplish the first task (sections II–IV), and make a start on the second (sections V–VIII).

Utilities and Quality-Adjusted Life Years

1989 ◽  
Vol 5 (4) ◽  
pp. 559-575 ◽  
Author(s):  
George W. Torrance ◽  
David Feeny
Keyword(s):  
Quality Of Life ◽  
Cost Utility ◽  
Health Improvement ◽  
Quality Adjusted Life Years ◽  
Utility Analysis ◽  
Quality Adjustment ◽  
Life Years ◽  
Quality Adjusted Life ◽  
Broad Overview

Utilities and quality-adjusted life years (QALYs) are reviewed, with particular focus on their use in technology assessment. This article provides a broad overview and perspective on these two techniques and their interrelationship, with reference to other sources for details of implementation. The historical development, assumptions, strengths/weaknesses, and applications of each are summarized.Utilities are specifically designed for individual decision-making under uncertainty, but, with additional assumptions, utilities can be aggregated across individuals to provide a group utility function. QALYs are designed to aggregate in a single summary measure the total health improvement for a group of individuals, capturing improvements from impacts on both quantity of life and quality of life– with quality of life broadly defined. Utilities can be used as the quality-adjustment weights for QALYs; they are particularly appropriate for that purpose, and this combination provides a powerful and highly useful variation on cost-effectiveness analysis known as cost-utility analysis.

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