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 (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.
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 (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.
PDB15 TOTAL CHOLESTEROL, HDL CHOLESTEROL AND HBA1C AFFECT QUALITY-ADJUSTED LIFE YEARS AND HOSPITAL COSTS FOR PATIENTS WITH TYPE 2 DIABETES IN THE UNITED KINGDOM–THE CARDIFF DIABETES COST UTILITY MODEL
