Valuing Health for Regulatory Cost-Effectiveness Analysis (2006) / Chapter Skim
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Appendix A Summary of Case Studies
Appendix A Summary of Case Studies
Pages 191-244
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Appendixes
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The Committee chose to conduct three case studies that reflect the data and analytic approaches applied by different regulatory agencies as well as the diverse health impacts addressed. This appendix summarizes the case studies, which are described in more detail in three separate reports (Robinson et al., 2005a,b,c)
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2. The National Highway Traffic Safety Administration's (NHTSA's)
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· Third, we determined the QALY losses averted by the regulation. This step involved estimating the change in HRQL attributable to the injury or illness under two scenarios: a base case analysis that assumed that affected individuals would be in average health (adjusted for age)
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Below, we provide an overview of the methods we applied across all three case studies, focusing on the process used to describe the health endpoints and to compare HRQL with and without the condition of concern. In the health care field, "without condition" health (i.e., the health status of an individual in the absence of a particular illness or injury of concern)
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In each case study, we used at least one approach that involved expert assignment of the HRQL attributes for the illnesses or injuries of concern. Developing descriptions for these expert assignments involved several challenges.
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These valuation surveys are described in Chapter 3; see especially Table 3-4. In each case study, at least one of the HRQL approaches involved expert assignment of the attributes defined under a particular generic index.
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In sensitivity analysis, we also compared the "with condition" HRQL estimates to a value of 1.0. This latter comparison is equivalent to assuming that, in the absence of the illness or injury, the affected individuals would be in perfect or optimal health.5 These age-adjusted estimates of average population health use the same underlying community-based valuation survey for each index (as discussed in Chapter 3)
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; for ages 10 through 19, average health would be the midpoint between perfect health and the values estimated for ages 20 through 29; and for those older than the reported age 6EQ-5D estimated based on 2001 MEPS data by Dr. William Lawrence, Agency for Healthcare Research and Quality.
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ranges, average health would remain constant at the value reported for the eldest age group. This approach means that the HRQL impacts for young children will be the same regardless of whether the comparison is to perfect or average health, since a value of 1.0 is used for "without condition" HRQL in both cases.7 Table A-2 presents the estimates of average population health used in this analysis for selected ages, for males and females combined.
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Comparison to "With Condition" Values Based on Expert Assignment Many researchers hypothesize that experts responding to the sorts of questionnaires used in the case studies implicitly compare the condition to perfect health, rather than to average health for an individual of a given age. Our interviews of the experts involved in the case studies generally reinforced this impression; they reported that they considered the impacts of the illness or injury on someone who is otherwise in good health; i.e., does not have other conditions that affect their HRQL.
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Thus, in this latter comparison, we are overstating the impacts of the health condition both because the values reflect HRQL decrements other than those related to the condition itself and because the affected individuals are not likely to be in perfect health throughout their lifetimes. For example, the average age of the Holbrook et al.
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Analysts assigned the QWB attributes (which reflect functional status and include symptom/problem codes) that best corresponded to the HRQL impacts for each of the health endpoints considered.
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We separated certain of the severe and chronic effects into subcategories to better reflect the varying health states that result, using data provided in FDA's analysis. This led to descriptions of 17 separate nonfatal endpoints, including 13 related to infections and 4 related to reactive arthritis, as listed in Table A-4 in the next section.
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The descriptions provided to the experts did not provide information on the average age of the affected individuals. We then sent these descriptions to the medical experts, along with a list of the domain and attribute definitions for each generic index and instructions for characterizing each endpoint in terms of the attribute lev
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coli infections, for which the average age at incidence was four years. For fatal cases and lifelong effects, we assumed that the average life expectancy of the affected individuals would extend with certainty to age 77, again consistent with FDA's approach.11 The FDA analysis (along with more recent studies)
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SOURCE: Case study team analysis of expert assignments provided in February to March, 2005.
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As previously discussed, we assume that the expert assignment implicitly involved comparison to perfect health and that the decrement from average health would represent the same proportional reduction. This table presents the results discounted at both a 3 and 7 percent discount rate, reflecting current guidance for discounting in regulatory analysis (OMB, 2003a)
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Salmonella, long-term reactive arthritis, chronic and unremitting 246 134 472 257 348 187 256 140 18. Premature mortality 41 23 40 23 38 22 35 20 Total 1,463 794 1,864 1,019 1,293 706 1,298 721 NOTES: Assumes that, in the absence of illness, health status would equal the average for the U.S.
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The table includes the results TABLE A-6 Juice Processing Case Study: Sensitivity Analysis for QALY Losses Case Study Expert Assessment (median) FDA Discount QWB Scenario Rate EQ-5D HUI-3 SF-6D QWB Results Total QALY losses compared to 3% 1,463 1,864 1,293 1,298 N/A average age-adjusted health 7% 794 1,019 706 721 Total QALY losses compared to 3% 1,659 2,121 1,563 1,700 perfect health 7% 882 1,136 843 924 888*
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The cost estimate in each of these calculations reflects compliance costs only, including both recurring costs and the annualized value of the initial costs.14 Medical cost savings are not considered. We then report the health-benefits-only ratio using each of the alternative approaches to estimating QALY losses; in this case, we net out the medical costs savings from the regulatory costs.
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TABLE A-7 Juice Processing Case Study: Cost-Effectiveness Ratios 3% Discount Rate 7% Discount Rate Averted deaths 2 deaths 2 deaths Averted life-year losses 47 years 27 years Regulatory compliance costs $26 million $28 million Compliance cost per fatality averted $13 million $14 million Compliance cost per life year $560,000 $1.0 million gained EQ-5D HUI-3 SF-6D QWB EQ-5D HUI-3 SF-6D QWB Averted QALY losses 1,500 1,900 1,300 1,300 790 1,000 700 720 QALYs QALYs QALYs QALYs QALYs QALYs QALYs QALYs Regulatory compliance costs, net of $22.0 million $23.4 million health treatment savings Health-benefits-only ratio $16,000 $13,000 $18,000 $18,000 $29,000 $23,000 $33,000 $32,000 per QALY per QALY per QALY per QALY per QALY per QALY per QALY per QALY NOTES: Reflects new incidence averted by a single year of full implementation of the rule, dollar year not reported. Assumes that, without the pathogen-related illness, health status will be the same as the average for the U.S.
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Because a higher discount rate reduces the impact of future year QALY losses, the costs per QALY are higher under a 7 percent discount rate than under the 3-percent rate. All of these ratios would show lower costs per QALY if the results of our sensitivity analysis were used, because the comparison to perfect health increases the estimates of QALY gains.
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For example, the developers of each index calculated relative health state index values using different population surveys (see Table 3-4) , and another set of population surveys were the basis for the Committee's estimates of average age-specific health under each index (see Table A-2)
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The ELS values for each AIS category are calculated periodically based on data for all types of crashes nationally, then applied across the 15The QALY losses are based on an index adapted especially for crash-related injuries (Miller et al., 1991) , rather than on one of the generic indexes used elsewhere in this case study.
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At the time that the analysis was completed, OMB recommended application of a 7-percent discount rate, which led to a cost per ELS ranging from $2.1 to $3.7 million. In this analysis, NHTSA did not report a total dollar value for all of the injuries and fatalities averted by the rule, and hence did not calculate net benefits (benefits minus costs)
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While this system includes data on thousands of crash victims, only 22 of the sampled cases involved injuries to children in child restraints. These sample cases represent roughly 1,752 cases nationwide (including 160 that are immediately fatal)
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Cerebrum subarachnoid hemorrhage g. Vault skull fracture comminuted 124.43 Nonfatal (hospitalized)
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skull fracture NFS f. Vault skull fracture comminuted g.
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SOURCE: NASS-CDS data provided by Jim Simons, NHTSA, December 7, 2004. The NASS-CDS data did not include information on the duration of the injury or on life expectancy (NHTSA, 2002b)
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We used discounting only to reflect the time value of averting the future year HRQL impacts associated with an injury that occurs in the current year; we did not discount the different years of incidence in the NASS-CDS data set. Our assessment of HRQL impacts involved the use of four generic indexes.
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. The next step in the analysis involved estimating the QALY losses that could be avoided if all of these injuries were averted by a hypothetical regulation.
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Table A-10 provides the resulting estimates of total QALY losses for the EQ-5D, HUI-2, and QWB, assuming that normal health (in the absence of the injury) would equal average population health for an individual of the same age, and applying both 7 and 3 percent discount rates.
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Our sensitivity analysis, presented in Table A-11, indicates that comparison to perfect health (a value of 1.0) rather than average health increases the estimates of QALY losses across the different approaches, as expected.
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Total QALY losses 3% 8,998 8,305 11,236 compared to average 7% 4,629 4,263 5,992 age-adjusted health Total QALY losses 3% 9,717 9,040 19,862 compared to perfect 7% 4,832 4,469 9,822 health Because of the data limitations discussed earlier, for the FCI we compare the 12-month values to the 12-month values for the other three indexes, rather than using it to assess lifetime effects. We focus on the five cases with injuries that were identified (by MacKenzie)
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However, we believe that one of the major sources of uncertainty in this case study relates to the use of adult health state index values for children. While the use of adult values is often necessitated by limitations in the available data, it raises difficult practical and ethical questions as discussed in more detail in the main text of this report.
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benefits, as well as significant health and environmental benefits that could not be quantified. In this case study, we considered a subset of the cardiovascular and respiratory effects included in EPA's analysis, focusing on those endpoints that account for the majority of the monetized benefits of the rule: preventable mortality, chronic bronchitis, and cardiac disease following nonfatal acute myocardial infarction (AMI)
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EPA adjusted these estimates to reflect the effects of real income growth over time and the lag between exposure reduction and reduction in mortality rates. For chronic bronchitis and restricted activity days, EPA adapted dollar values from stated preference studies of individual WTP.
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22 $150 Chronic bronchitis (adults, 26 and over) 5,600 $2,400 Nonfatal myocardial infarctions (adults, 18 and 15,000 $1,200 older)
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In our calculations, we assumed the chronic bronchitis would last for the remainder of the affected individuals' lifespan but did not consider its effects on life expectancy nor model the likely worsening of symptoms over time.24 Our assessment of life expectancy used conditional survival rates as in the NHTSA case study, similar to the approach used in Hubbell (2004) and other EPA analyses.
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We adjusted the population average conditional survival rates using different factors for AMI cases with and without congestive heart failure. Consistent with Hubbell (2004)
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Estimates of QALY Gains The three approaches applied in this case study addressed different respiratory and cardiovascular endpoints broken out in different ways. Our expert assignment approach used 25 subcategories characterized by severity and symptoms; our application of the MEPS-based catalogue used four ICD codes (one for chronic bronchitis and three for cardiac disease)
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. For the values taken from the CEA Registry studies, which scenario results in larger estimates depended on age, because we anchored the percentage reduction from average population health at the average age of the underlying study samples.
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cFor the EQ-5D MEPS catalogue, numerical decrements from average health are assumed to be constant across all years of age, and the difference between "without condition" average and perfect health is added to this decrement for the perfect health comparison. dFor the transfer from the CEA Registry studies, "with condition" health is assumed to be a constant fraction of "without condition" health; this fraction is calculated based on the average age of the samples used in each study.
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For TABLE A-15 Nonroad Diesel Emissions Case Study: QALY Losses, All Cases 3% 7% HRQL Approach/Endpoint Discount Rate Discount Rate Expert Assignment of EQ-5D Attributes Nonfatal chronic bronchitis 16,245 9,966 Nonfatal AMI 10,259 7,823 Preventable mortality 92,852 63,605 Total 119,356 81,395 EQ-5D MEPS Catalogue Nonfatal chronic bronchitis 7,136 4,341 Nonfatal AMI 8,848 6,402 Preventable mortality 92,852 63,605 Total 108,837 74,349 Transfer from Selected Studies Nonfatal chronic bronchitis 6,028 3,699 Nonfatal AMI 15,246 10,782 Preventable mortality 92,852 63,605 Total 114,126 78,086 NOTES: Assumes that, in the absence of illness, health status would equal the average for the U.S. population in the same age group.
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associated with treatment of the conditions. TABLE A-16 Nonroad Diesel Emissions Case Study: Sensitivity Analysis for QALY Losses Discount EQ-5D Expert EQ-5D MEPS Transfer from Scenario Rate Assignment Catalogue Selected Studies Total QALY losses 3% 119,356 108,837 114,126 compared to average 7% 81,395 74,349 78,086 age-adjusted health Total QALY losses 3% 154,447 186,785 173,160 compared to perfect 7% 104,666 125,292 116,638 health
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All of the cost per QALY estimates would be lower if we used the results of our sensitivity analysis, since the comparison to perfect health yields larger estimates of QALY losses. Again, this case study does not fully reflect certain of the Committee's recommendations.
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Assumes that, without the pollution-related illness, health status would be the same as the average for the U.S. population in the same age group.
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We also found that the MEPS catalogue used in the EPA case study was quite useful for this sort of analysis; it provides U.S. population health state index values for a variety of conditions encountered in many regulatory analyses.
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Furthermore, the analyses show the importance of comparing "with condition" values to measures of expected actual "without condition" health; comparisons to perfect health lead to estimates of QALY losses that are misleadingly large in some cases. Finally, we were not able to assess whether alternative HRQL approaches would change regulatory decisions.
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Contributors: John Anderson, University of California, San Diego; Barbara Altman, National Center for Health Statistics; Fred Angulo, Centers for Disease Control and Prevention; Lawrence Deyton, M.D., Veteran's Administration; Sherine Gabriel, M.D., Mayo Clinic; Janel Hanmer, University of Wisconsin-Madison; William Lawrence, M.D., Agency for Healthcare Research and Quality; Gwen Wanger, M.D., Beth Israel Deaconess Medical Center. Expert application of generic indexes: Infectious disease -- Claire Panosian, M.D., David Geffen School of Medicine, University of California, Los Angeles (UCLA)
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Other advisers: Larry Blincoe, National Highway Traffic Safety Administration; Jim Simons, National Highway Traffic Safety Administration; Carmen Brauer, M.D., Harvard School of Public Health. Contributors: John Anderson, University of California, San Diego; Barbara Altman, National Center for Health Statistics; Nancy Bondy, National Highway Traffic Safety Administration; David Feeny, Kaiser Permanente; Janel Hanmer, University of Wisconsin-Madison; Troy Holbrook, University of California, San Diego; Robert Kaplan, University of California, Los Angeles; William Lawrence, M.D., Agency for Healthcare Research and Quality; Ellen MacKenzie, Ph.D., Johns Hopkins University; Bryce Mason, Rand Corporation; Ted Miller, Pacific Institutes for Research and Evaluation; Ryan Palugod, Institute of Medicine; William Rhoads, Centers for Disease Control and Prevention; Jon Walker, National Highway Traffic Safety Administration.
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Environmental Protection Agency; Janel Hanmer, University of Wisconsin-Madison; Fernando Holguin, Centers for Disease Control and Prevention; William Lawrence, M.D., Agency for Healthcare Research and Quality; Darwin LaBarthe, Centers for Disease Control and Prevention; Jim Neumann, Industrial Economics, Incorporated; Peter Neumann, Harvard School of Public Health; Nathalie Simon, U.S. Environmental Protection Agency; Patrick Sullivan, University of Colorado.
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