B.: 2001, ‘The Value of Remaining Lifetime is Close to Estimated Values of Life

Abstract Workers under 50 on average will spend 10-20% of their future hours working. So, assuming they value leisure time at the wage rate, the value of their lives is 5-10 times their future lifetime earnings. This value is close to values of life estimated by compensating wage differentials or willingness to pay. © 2001 Elsevier Science B.V. All rights reserved. The three studies based on large surveys of hypothetical willingness-to-pay for increased safety gave values around US$ 3 million per statistical life. Viscusi (1992) notes (as have many others), "These numbers greatly exceed the workers lifetime earnings-roughly by an order of magnitude." Standard economic models of labor supply assume that the value of leisure time at the margin is equal to the marginal wage rate. If we assume the value of all time is equal to the wage rate, we can calculate a value of life by multiplying the wage rate by total discounted hours of life remaining. This is a lower bound if there are diminishing returns to leisure

Discounting of Life-Saving and Other Nonmonetary Effects

Cost-effectiveness analysts generally assume that preferences over time are such that streams of monetary and nonmonetary program effects can be reduced to one discounted sum of monetary costs and another of effects. It is known that if the nonmonetary effects can be cashed out in a way that does not vary with time, then the rates of discount for monetary and nonmonetary effects have to be equal. This paper presents a more compelling argument for the equality of those rates when hard to monetize benefits such as life-saving are involved. It shows that if the ability to produce the nonmonetary effect does not diminish too quickly over time, failure to discount benefits implies that programs are always improved by delay. In general, discounting benefits and costs at different rates can lead to peculiar results.cost-effectiveness analysis, philosophy of modeling

Algorithms for Health Planners: Vol. 6, Hypertension

Assists health planners, especially those in Health Systems Agencies (HSAs), in developing objectives and taking action toward ameliorating the problem of hypertension, or high blood pressure (HBP). The algorithm, or methodology, helps HSAs assess the prevalence of hypertension in their areas, identify breakdowns in the health care system, and specify needed improvements in the system. Known facts about high blood pressure, its pathological complications, prevalence, and treatment are reviewed. Guidelines are presented for assessing community HBP rates (through analysis of existing data or through collection of new data) and comparing those rates with national or other standards. Possible community actions toward ameliorating these problems are discussed: educational programs, door-to-door screening, industrial clinics, neighborhood clinics, and private screening. Finally, guidelines are presented for assessing the effects of such community programs

Options for handling missing data in the Health Utilities Index Mark 3.

BackgroundThe Health Utilities Index Mark 3 (HUI3) is a tool composed of 41 questions, covering 8 attributes: vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain. Responses to these questions can define more than 972,000 health situations. This tool allows respondents to answer "Don't Know," for which there is no scoring instruction, to any given question. This situation creates a break in the scoring algorithm and leads to considerable amounts of missing data. The goal of this study is to develop strategies to deal with HUI3 scores for participants who have missing data.MethodsThe authors used data from 248 individuals enrolled in the Cataract Management Trial, focusing on the HUI3 vision and ambulation attributes, which had 19% and 10% of attribute levels missing, respectively. Inspection and deduction were used to fill in values independent of the value of the missing data, then alternative analytic techniques were compared, including mean substitution, model scoring, hot deck, multiple imputation, and regression imputation.ResultsInspection and logical deduction reduced the percentage of missing information in the HUI3 by 49% to 87%. A comparison of analytic techniques used for the remaining HUI3 vision data missing demonstrated the value of building models based on internal response patterns and that simple analytic techniques fare as well as more complicated ones when the number of missing cases is small.ConclusionAnalyzing the pattern of responses in cases where the attribute level score is missing reduces the amount of missing data and can simplify the analytic process for the remaining missing data