Appendix F
The Expected Population Value of Quality Indicator Reporting (EPV-QIR): A Framework for Prioritizing Healthcare Performance Measurement

David O. Meltzer, MD, PhD, and Jeanette W. Chung, PhD

The University of Chicago

I.
INTRODUCTION

In “The Opportunity Costs of Haphazard Social Investments in Life-Saving,” Tengs and Graham (1996) studied the costs and benefits of 185 interventions that reduce the risk of premature mortality to evaluate the allocative efficiency of investment in life-saving opportunities in the United States. According to their estimates, the United States spent approximately $21 billion on life-saving interventions that prevented roughly 56,700 premature deaths. However, reallocating those dollars using cost-effectiveness criteria to maximize the number of lives saved could have avoided an additional 60,200 premature deaths.

Tengs and Graham’s analysis provides a cautionary tale for stakeholders in healthcare quality improvement, patient safety, and disparities. There are currently more than 1,400 measures in the U.S. Department of Health and Human Services (HHS) National Quality Measures Clearinghouse (NQMC) and more than 250 measures in the Agency for Healthcare Research and Quality (AHRQ) National Healthcare Quality and Disparities Reports (NHQR and NHDR). Given limited resources and an ever-proliferating set of healthcare measures, Tengs and Graham’s analysis reminds us of the importance of asking whether we are maximizing the returns on our investments that seek to improve healthcare quality and safety.

This paper proposes a conceptual and methodological approach to quantifying the population value of efforts to improve quality and reduce disparities, specifically through the selection of quality and disparities indicators such as the AHRQ National Healthcare Quality and Disparities Reports that are the subject of this IOM Committee. To do so, the paper draws upon the literature using measurement approaches from medical cost-effectiveness analysis to prospectively assess the value of research (Claxton and Posnett, 1996; Fenwick et al., 2008; Meltzer, 2001). The result is an approach to estimate the expected population value of quality indicator reporting (EPVQIR). Although analytic tools of cost-effectiveness analysis are used, our approach recognizes that “identifying and issuing guidance regarding the use of cost-effective health technologies does not, in itself, lead to cost-effective services provision” (Fenwick et al., 2008). This gap between evidence on the potential cost-effectiveness of an intervention and the cost-effectiveness of its implementation in practice can arise for many reasons. One reason is uncertainty about the costs and benefits of an intervention. In such cases, modeling the expected value of research has led to useful applications in prioritizing research agendas in domains including Alzheimer’s disease treatments (Claxton et al., 2001), antipsychotic drugs in schizophrenia (Meltzer et al., 2009), bronchodilators in chronic obstructive pulmonary disease (Oostenbrink et al., 2008), and anti-platelet medications in cardiac care (Rogowski et al., 2009). However, while uncertainty in the effectiveness of interventions is relevant in addressing



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 175
Appendix F The Expected Population Value of Quality Indicator Reporting (EPV-QIR): A Framework for Prioritizing Healthcare Performance Measurement David O. Meltzer, MD, PhD, and Jeanette W. Chung, PhD The University of Chicago I. INTRODUCTION In “The Opportunity Costs of Haphazard Social Inestments in Life-Saing,” Tengs and Graham (1996) studied the costs and benefits of 185 interventions that reduce the risk of premature mortality to evaluate the allocative efficiency of investment in life-saving opportunities in the United States. According to their estimates, the United States spent approximately $21 billion on life-saving interventions that prevented roughly 56,700 premature deaths. However, reallocating those dollars using cost-effectiveness criteria to maximize the number of lives saved could have avoided an additional 60,200 premature deaths. Tengs and Graham’s analysis provides a cautionary tale for stakeholders in healthcare quality improvement, patient safety, and disparities. There are currently more than 1,400 measures in the U.S. Department of Health and Human Services (HHS) National Quality Measures Clearinghouse (NQMC) and more than 250 measures in the Agency for Healthcare Research and Quality (AHRQ) National Healthcare Quality and Disparities Reports (NHQR and NHDR). Given limited resources and an ever-proliferating set of healthcare measures, Tengs and Graham’s analysis reminds us of the importance of asking whether we are maximizing the returns on our investments that seek to improve healthcare quality and safety. This paper proposes a conceptual and methodological approach to quantifying the population value of efforts to improve quality and reduce disparities, specifically through the selection of quality and disparities indicators such as the AHRQ National Healthcare Quality and Disparities Reports that are the subject of this IOM Commit - tee. To do so, the paper draws upon the literature using measurement approaches from medical cost-effectiveness analysis to prospectively assess the value of research (Claxton and Posnett, 1996; Fenwick et al., 2008; Meltzer, 2001). The result is an approach to estimate the expected population alue of quality indicator reporting (EPV- QIR). Although analytic tools of cost-effectiveness analysis are used, our approach recognizes that “identifying and issuing guidance regarding the use of cost-effective health technologies does not, in itself, lead to cost-effec - tive services provision” (Fenwick et al., 2008). This gap between evidence on the potential cost-effectiveness of an intervention and the cost-effectiveness of its implementation in practice can arise for many reasons. One reason is uncertainty about the costs and benefits of an intervention. In such cases, modeling the expected value of research has led to useful applications in prioritizing research agendas in domains including Alzheimer’s disease treatments (Claxton et al., 2001), antipsychotic drugs in schizophrenia (Meltzer et al., 2009), bronchodilators in chronic obstructive pulmonary disease (Oostenbrink et al., 2008), and anti-platelet medications in cardiac care (Rogowski et al., 2009). However, while uncertainty in the effectiveness of interventions is relevant in addressing 

OCR for page 175
 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS quality and disparities, quality and disparities reporting is more often targeted at variability in the implementation of available information. Recently, value of research approaches have been adapted to address issues of imperfect implementation (Fenwick et al., 2008; Hoomans et al., 2009). The expected population value of quality indicator reporting (EPV-QIR) we propose is intended to be a useful tool in selecting quality indicators that can produce the largest improvements in population health. Quality indica - tors can be ranked in terms of their EPV-QIR and a set of indicators can be identified that offer the highest expected returns to investing in quality improvement. The EPV-QIR depends on several factors: 1. The net health benefit of the appropriate implementation of the intervention, which is the magnitude of the potential health benefit of the intervention (measured in quality adjusted life years (QALYs)) net of the opportunity costs in health when the intervention is fully implemented to maximize its benefit net of costs, 2. The size of the population of persons who should receive the intervention given the standard of care, e.g., those with a positive net health benefit from the intervention, 3. The current state of implementation, which potentially includes both the rate of utilization among parts of the population with positive net health benefits and the rate of use among those parts of the population with negative net health benefits (for whom there are potential gains in net health benefits that can be obtained by eliminating inappropriate use in that population), and 4. The potential for quality improvement, especially as produced by reporting quality indicators. This depends on the probability that providers (or patients) will make choices likely to improve quality when given information on provider performance is provided, and the effectiveness of existing quality improve - ment interventions to improve outcomes. Because data on these effects may be especially incomplete, our approach also specifically highlights uncertainty in the extent to which quality reporting will stimulate quality improvement action, and quality improvement action will change implementation. This includes both estimating the expected (average) effects of reporting on quality, and bounding estimates of these effects when data on the effectiveness of reporting on quality is especially incomplete. For example, if an intervention is not currently used or at least not used in persons in whom it produces net harms, one such bound would the value of perfect implementation, which is the total benefit that can be achieved in a population if everyone who should receive an intervention receives it and everyone who should not receive an intervention does not receive it. We explicate our framework in detail in the remainder of this paper, and demonstrate its application in cal - culating the expected value of quality improvement for selected NHQR measures. We develop our framework in Section II, progressively developing concepts that are critical to the EPV-QIR framework. In Section III, we demonstrate the EPV-QIR calculations for selected measures in the NHQR, while also paying close attention to opportunities to bound estimates of EPV-QIR with more limited data. In Section IV, we discuss the scope of potential application for the EPV-QIR method and its limitations and implementation issues. Section V concludes with a discussion of areas for future development. II. THE EPV-QIR FRAMEWORK Our framework begins with the assumption that all measures are based explicitly or implicitly on some stan - dard of care, which we denote by S. We use O to denote all other alternatives, which could include some other standard of care, or “usual care” or “doing nothing.” Our model could easily be generalized to include multiple alternative standards of care (Oi) by indexing groups additionally according to the care they receive currently. For simplicity, however, we develop our theoretical framework in the case in which there is only a single alternative current pattern of care. Given this single current pattern of care, the incremental benefit of S is the difference between the effectiveness of the standard of care (eS) and the effectiveness of the alternative (eO) current pattern. The incremental benefit of

OCR for page 175
 APPENDIX F S can be written as De = eS – eO. The incremental cost of S is the difference between the cost of the standard of care (cS) and the cost of the alternative (cO). The incremental cost of S can be written as Dc = cS – cO. Net Health Benefit (NHB). The net health benefit of the standard of care (NHBS) relative to O, is the incre- mental health benefits of the standard of care net of its incremental costs, where costs are denominated in units of foregone health benefits due to the financial costs of the standard of care (Stinnett and Mullahy, 1998): Dc NHBS = De − [Eq. 1] l In Eq. 1, l is a society’s threshold willingness-to-pay for an additional unit of health benefit, which might be measured in life years or quality-adjusted life years (QALYs).1 In these cases, l would be the amount of money that society is willing to pay to save an additional life year or quality-adjusted life year. The term Dc/l is in units of health benefits and represents the foregone health benefits that could have been obtained by allocating money to some marginally cost-effective standard of care. In other words, Dc/l represents the opportunity costs in terms of health of accomplishing the standard of care. When an intervention is cost-effective, so its incremental cost- effectiveness ratio (ICER) OCR for page 175
 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS is the total net health benefits that can be gained by improving implementation from current rates to 100%. Max- PVQI is simply the difference between PVPI and PVCI, or MaxPVQIS = PVPIS – PVCIS = NS × (1 – rSC) × NHBS [Eq. 4] This MaxPVQI defines the maximum population net gain in health from adopting some standard of care relative to the absence of that standard, in essence providing the net health benefits of the intervention to the fraction (1 – rSC) of the population who should receive the intervention who are not currently receiving it. Inappropriate Use and Oeruse. As noted above, these same general equations can be used to estimate the value of quality improvement when there are multiple other patterns of care, as in the case in which an intervention is overused or inappropriately used, for example. The adjustments that are needed in such cases are to define the relevant population in terms of their current (inappropriate) treatment and then to measure the net health benefit of the change to the current standard of care relative to that inappropriate care. The net health benefit of S imple- mented within the measure population to which S is meant to apply will not be the same as the net health benefit of implanting S in another population. Hence, calculating the EPV-QIR of measures of overuse or inappropriate use will require estimates of the costs and health effects of implementing the standard in patients outside the measure population. Because the focus of the AHRQ quality indictors is on increasing appropriate use, we do not focus on overuse in our primary exposition, but we do discuss in Appendix A how our analysis can be extended to incorporate overuse and illustrate one calculation incorporating overuse. Expected Population Value of Quality Improement (EPV-QI). The MaxPVQI assumes that both the current rate of implementation is known and that quality improvement results in 100% implementation. The expected popula- tion alue of quality improement (EPV-QI) reflects the fact that there may be uncertainty about several aspects of the process by which quality initiatives will improve population outcomes. In particular, both the current levels of implementation and the extent to which quality improvement efforts will improve implementation. Indeed, it is well recognized that quality improvement approaches are generally not 100% effective in raising performance to levels of perfection (Oxman et al., 1995). To characterize the uncertainty in this imperfect implementation both before and after QI efforts, let rSC eQI and rSC stQI be the rates of implementation before and after some QI initiative Pr Po so that DrSC = rSC – rSC is the change in implementation before and after the intervention. Because these ele - PostQI Pr eQI ments and their change can be uncertain, we reflect this uncertainty by assuming the change in implementation with a quality improvement effort (DrSC ) is distributed f(DrSC ) so that the expected extent of quality improvement QI QI would be ∫ DrSC dc and the expected population value of quality improvement would be: QI EPV − QI = ∫ N S × DrSQI × NHBS dc = N S × NHBS × ∫ DrSQI dc [Eq. 5] C C Expected Population Value of Quality Indicator Reporting (EPV-QIR). A crucial element in the consideration of quality reporting and the reporting of other indicators is that they do not themselves change quality but instead depend on some sort of action model by which reporting leads to changes in the behavior of providers or others that can improve quality. Fully specifying such an action model is beyond the scope of this paper, but Figure 1 provides some potentially salient elements of such a model, including that quality reporting would need to produce changes in behavior by either providers or patients in order to produce improvements in quality. Because such changes in behavior are unlikely to completely realize potential quality gains (Schneider and Epstein, 1996), it is important to account for the likelihood that the gain in implementation with quality reporting will generally be less than DrSC . We denote this gain in implementation with quality reporting as DrSC , and for simplicity assume QI QR that the uncertainty in how reporting will effect quality can be represented by a probability of undertaking qual - ity improvement action, pQI, so that DrSC = ∫ DrSC dc × pQ is the expected change in implementation with quality QR QI reporting and the expected population value of quality reporting is: EPV − QIR = N S × NHBS × ∫ DrSC dc × p QI QI [Eq. 6]

OCR for page 175
 APPENDIX F Change in Quality Population Indicator Health Benefits / Reporting Cost Savings Probability that information will lead to QI is variable Quality Change in Rates Improvement of Performing Action to Change Standard of Ef fectiveness of QI in Provider Care changing provider Behavior behavior is variable FIGURE 1 Conceptual Model for the Expected Population Value of Quality Indicator Reporting NOTE: Patients may also change behavior based on quality indicator reporting, for example by selecting high-quality providers, causing changes in the rates at which care delivered meets standards of care. Figure F-1 R01677 This equation provides our fundamental framework for developing estimates of the value of quality reporting editable vectors efforts. Summary of EPV-QIR Framework The EPV-QIR framework provides a method for estimating the expected value of improving quality for existing quality measures, measured in units of net health benefits that can be gained within a specified population. This method can be used to estimate the potential value of improving performance on existing quality measures, which can then be used to prioritize measures for reporting or for other investment in quality improvement. Figure 2 provides a summary of the EPV-QIR approach. First, we assume that reporting on a quality measure leads to qual - ity improvement action with probability pQ. The effectiveness of a quality improvement action is the effect size of that action, or DrSC. The population alue of perfect implementation (PVPI) is equal to the net health benefit that can be achieved by improving quality on a measure to perfect or 100% levels of performance. The expected alue of quality improement (EPV-QIR) is the product of the likelihood quality reporting leads to quality improvement efforts, the improvement in implementation that comes from these quality improvement efforts, and the PVPI. Thus, the expected value of quality improvement for a specific quality indicator depends on the probability that quality improvement efforts will be undertaken, the effectiveness of those efforts, and the maximum potential gain in population net health benefits that can be achieved by closing the quality gap for that measure. The EPV-QIR will equal the PVPI only when reporting a quality measure will result in quality improvement action with certainty and that quality improvement action is 100% effective in perfecting performance. Thus the PVPI and, if current implementation is known, the MaxPVQI, provide bounds on the EPV-QIR. Quality Quality Change in Quality Population Net Improvement Improvement Indicator Health Benefit from Action Action Changes Reporting Quality Undertaken Care Improvement EPV-QIR S = π Q ∆ r SC * MaxPVQI S * FIGURE 2 Conceptual Summary of the EPV-QIR Approach Figure F-2 R01677 editable vectors

OCR for page 175
0 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS III. USING THE EPV-QIR FRAMEWORK TO PRIORITIZE MEASURES Using the EPV-QIR framework to prioritize measures ideally requires data on all the elements included in Eq. 6. Because all the other elements depend on defining an intervention in terms of its net health benefits, we begin by exploring the data requirements for NHB and then proceed to defining the other elements. Along the way, we also elaborate on these opportunities identified above in which it may be possible to bound the EPV-QIR using more limited data. Net Health Benefits. Calculating an estimate of NHBS requires information on: (1) the total cost of implement- ing the standard of care per person (or per unit, e.g., per infection avoided); (2) the effectiveness of implementing the standard of care per person (or per unit, e.g., per infection avoided); (3) the total cost of implementing the comparator per person (or per unit, e.g., per infection avoided); (4) the effectiveness of implementing the compara - tor per person (or per unit, e.g., per infection avoided); and (5) the societal cost-effectiveness threshold. As noted, the societal cost-effectiveness threshold is generally varied across a range of values reflecting the uncertainty in this value from the literature. Items 1-4 may be obtained from published cost-effectiveness studies evaluating the standard of care against the comparator, if such studies exist. Preference should be given to cost-effectiveness studies conducted in a population that is similar, if not the same, as the population defined by the denominator of the measure in question. For example, for the NHQR measure, “Percent of individuals age 65+ who ever received a pneumococcal vaccination,” a cost-effectiveness study evaluating the pneumococcal vaccination among adults age 45-55 would be less ideal than a cost-effectiveness study evaluating vaccination among adults age 65-85. Preference might also be given to cost-effectiveness studies conducted in U.S. populations, because difference in healthcare systems might influence total costs of implementing a particular treatment or standard of care. This will affect the validity of net health benefit estimates. It is essential that cost-effectiveness studies publish sufficient data to assess effects on both costs and effectiveness in QALYs for the standard of care/comparator in question. Cost- effectiveness studies that only publish cost-effectiveness ratios (dollars per QALY) are not sufficient to calculate NHB because neither costs nor effectiveness is known. Number of Indiiduals Eligible for the Standard of Care. In order to calculate these population-based measures, it is necessary to have an estimate of the number of individuals eligible for the standard of care. In other words, it is necessary to have an estimate of the size of the denominator population. If maximizing population health remains the goal, the eligible population is best selected when the population is defined as that within which the intervention is cost-effective, but if another population is chosen for any reason then the size of that population should be used. To use the same example above, calculating the VPI for the measure “Percent of individuals age 65+ who ever received a pneumococcal vaccination” requires an estimate of the total number of individuals in the U.S. age 65+. For some public-health population-based measures, estimates of the eligible population may be as simple as obtaining age-group and perhaps sex-specific population estimates from the U.S. Census Bureau. For measures denominated on the basis of healthcare utilization such as hospitalizations, weighted population estimates of services and utilization from national healthcare surveys such as the National Hospital Discharge Survey (NHDS) may be useful. For measures defined on the basis of a specific clinical process of care, estimating the size of the denominator population may require estimates of the prevalence of certain conditions. Rate of Current Implementation. The rates at which individuals in a population receive indicated standards of care are reflected by quality indicators. The denominator of the measure is equal to the measure population (NS, as defined above), and the numerator of the measure is equal to the number of individuals in the measure population who received the standard of care within some reporting period—i.e., for whom “the standard was met,” N NSM × MS = rSC = SM . NS This data would typically be available for existing quality measures that had previously been collected, allow - ing for efforts to characterize the maximum potential improvements from existing levels of quality ( MaxPVQI). Sources of data for implementation rates include: the National Healthcare Quality Report (NHQR) itself, the Behavioral Risk Factor Surveillance Survey (BRFSS), and other quality reports. For new measures being considered about which nothing is known, less informative bounds based on the population alue of perfect implementation (PVPI) might be the most informative bound possible.

OCR for page 175
 APPENDIX F Expected Quality Improements. To develop more precise estimates of the EPV-QIR, it is necessary to know the probability of quality improvement (pQ) and the effect size of quality improvement interventions (DrSC).2 Probability of Quality Improement (pQ). One key factor is how providers might approach quality improve- ment when faced with new quality indicators. Understanding the distribution of quality improvement modalities in the provider population is necessary to derive an aggregate estimate of the effect size—i.e., the amount of change in provider behavior and performance rates that can be expected, conditional on a decision to undertake quality improvement. Indeed, studies have pointed to the heterogeneity of quality improvement efforts undertaken at the provider level (Bradley et al., 2005). As a result, information about both the range and potential effectiveness of these quality improvements efforts will very often be lacking. Indeed, there are several reasons to believe that pQ is much less than 1, as there is relatively little evidence supporting a strong direct link between public reporting and quality improvement activities (Epstein, 2006; Fung et al., 2008; Matthews et al., 2007; Robinowitz and Dudley, 2006). Part of the weak link may be attributable to the finding that hospitals and physicians often discount report cards on the basis of methodology, suggesting that in some cases performance reporting may have little direct effect on provider propensity to engage in targeted quality improvement efforts (Rainwater et al., 1998; Romano et al., 1999; Schneider and Epstein, 1996). A second issue complicating the link between public reporting of quality indicators and quality improvement action is that public reporting has often been studied in the context of pay for performance, making it difficult to parse out the independent effect of public reporting on provider quality improvement activities and/or outcomes (Lindenauer et al., 2007; Rodriguez et al., 2009). Finally, insofar as the existing literature has primarily focused on state-level or payer-specific reporting programs, it seems unlikely that responses to quality measures reported aggregated to the national level would elicit a stronger response to initiate focused quality improvement initiatives. Effectieness of Quality Improement (DrSC). There are numerous studies of the effectiveness of quality improvement programs (e.g., systems-based interventions to improve cancer screening [Carney et al., 1992; Carpiano et al., 2003]), general approaches to practice/provider behavior change (e.g., continuing medical education [Davis et al., 1995], educational outreach [O’Brien et al., 2007]), and/or specific tools (e.g., printed educational materials [Farmer et al., 2008]) in the context of specific standards of care or clinical conditions (Arnold and Straus, 2005; Renders et al., 2001). However, even when there is some evidence on the efficacy of these approaches, it is unlikely that they will be equally effective in improving performance across different standards of care. Summary. The relative paucity of evidence on the likely effectiveness of quality reporting on quality improve - ment activities and of quality improvement activities on implementation of standards of care suggest that efforts to quantify the EPV-QIR will have to rely heavily on bounds implied by estimates of the EPV-QI or MaxPVQI. EPV-QIR Calculations for Selected NHQR Measures Table 1 presents the results of attempts to estimate or bound EPV-QIR calculations for 14 NHQR measures for which we were able to obtain information on costs, effectiveness (in QALYs), denominator population, and cur- rent implementation rate. Appendix C lists the sources of data elements used in our calculations for each measure. Because of resource limitations, our primary goal in developing these estimates was to illustrate potential issues that could arise in the application of the EPV-QIR approach rather than to develop the best possible estimate for any one of these indicators. To facilitate discussion, we assigned a brief mnemonic to each NHQR measure in this report, listed in Column 1 of Table 1. Column 2 provides the measure definition for each NHQR measure. Column 3 shows the denominator population for each measure—i.e., the total number of individuals in the U.S. who should receive the standard of care for a given measure. Column 4 presents the total number of QALYs that can be achieved if all individuals in the denominator population received the standard of care—this is the popula- tion alue of perfect implementation (PVPI). Column 5 presents the total number of QALYs currently achieved given existing patterns of care in the population—this is the population alue of current implementation (PVCI). 2 Quality-adjusted life years (QALYs) are a unit of measurement that is used in quantifying the health benefits or effectiveness of healthcare interventions. QALYs reflect the notion that years of life lived in less-than-perfect health may not be valued as much as years of life lived in perfect health.

OCR for page 175
 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS TABLE 1 EPV-QIR Calculations for 18 NHQR Measures Denominator Pop. VPI Pop. VCI Pop. Max Mnemonic NHQR Measure Population (QALYs) (QALYs) VQI (QALYs) NHQR_DMHTN Percent of adults with diagnosed diabetes with 17,268,973 7,021,537 4,107,599 2,913,938 most recent blood pressure <140/80 mm/Hg NHQR_DMCHOL Adults age 40 and over with diagnosed 17,268,973 1,828,056 1,003,602 824,453 diabetes with total cholesterol <200 mg/dL NHQR_DMFOOT Adults age 40+ with diagnosed diabetes who 17,268,973 2,326,165 1,644,599 681,566 had their feet checked for sores or irritation in the calendar year NHQR_DMHBA1C Percent of adults with diagnosed diabetes 17,268,973 1,474,394 805,019 669,375 with HbA1c level >9.5% (poor control); <7.0 (optimal); <9.0 (minimally acceptable) NHQR_HIVEVER People ages 15-44 who ever received an HIV 126,006,034 529,704 241,545 288,159 test outside of blood donation NHQR_PAP3YR Percent of women (age 18 and over) who 15,272,448 2,120,558 1,903,757 216,801 report they had a Pap smear within the past 3 yrs NHQR_DMEYE Adults age 40+ with diagnosed diabetes who 17,268,973 414,132 247,237 166,895 received a dilated eye examination in the calendar year NHQR_CRC50EVERCOLON Adults age 50 and over who ever received a 14,992,188 366,829 219,454 147,375 colonoscopy, sigmoidoscopy, or proctoscopy NHQR_BRCA2YRMAMM Percent of women (age 40+) who report they 60,428,554 1,167,474 1,046,640 120,833 had a mammogram within the past 2 years NHQR_CRCBIFOBT Adults age 50 and over who received a fecal 6,895,908 253,938 152,497 101,441 occult blood test (FOBT) in the last 2 years NHQR_CAPVACC65EVER Percent of individuals age 65+ who ever 38,869,716 161,291 92,420 68,871 received a pneumococcal vaccination NHQR_BSICVC Bloodstream infections (BSIs) per 1,000 140,000 0 –27,809 27,809 VQI represents QALYs that can central venous catheter (CVC) placements be saved by using chlorhexidine silver sulfadiazine coated catheters (external coat) NHQR_BSICVC Bloodstream infections (BSIs) per 1,000 140,000 0 –27,095 27,095 VQI represents QALYs that can central venous catheter (CVC) placements be saved by using silver, platinum and carbon coated catheters NHQR_BSICVC Bloodstream infections (BSIs) per 1,000 140,000 0 –24,864 24,864 VQI represents QALYs that can central venous catheter (CVC) placements be saved by using chlorhexidine minocycline and rifampicin coated catheters NHQR_BSICVC Bloodstream infections (BSIs) per 1,000 140,000 0 –23,001 23,001 VQI represents QALYs that can central venous catheter (CVC) placements be saved by using chlorhexidine silver sulfadiazine coated catheters (internal + external coat) NHQR_AMIBB Percent of AMI patients administered beta 682,699 123,172 109,623 13,549 blockers prescribed at discharge

OCR for page 175
 APPENDIX F TABLE 1 Continued Denominator Pop. VPI Pop. VCI Pop. Max Mnemonic NHQR Measure Population (QALYs) (QALYs) VQI (QALYs) NHQR_HFACE Percent of hospital patients with heart failure 295,101 64,976 55,359 9,616 and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge NHQR_AMIACE Percent of AMI patients with LVSD 185,695 44,830 38,509 6,321 prescribed ACE inhibitor at discharge Column 6 presents the total number of QALYs that can be gained by improving performance on a measure to 100% compliance—this is the maximum population alue of quality improement (MaxPVQI), and it is equal to the difference between PVPI and PVCI. Table 2 sorts the 14 NHQR measures by descending order of PVPI. Perfect implementation of all 14 measures would yield a total of 17,852,224 QALYs. Nearly 40% of this total can be obtained by achieving perfect imple - mentation of blood pressure control among adults with diagnosed diabetes (NHQR_DMHTN measure). More than half of the total number of QALYs achievable can be obtained by perfecting implementation of both blood pressure control for adults with diabetes and ensuring annual optimal foot care for adults with diabetes. Examining these 14 NHQR measures alone, we see that perfect implementation of the top 7 measures would yield over 90% of total QALYs possible. Moreover, these high-impact measures are all concentrated in public health domains—diabetes, cervical cancer screening, breast cancer screening, and HIV testing. Table 3 lists the 14 NHQR measures in descending order of MaxPVQI. This table provides important comple- mentary insights to Table 2. Whereas Table 2 identifies those measures with the greatest net health benefit at the population level, Table 3 identifies those measures promising the greatest returns to additional quality improvement in terms of net health benefit. For example, as shown in Table 2, biennial mammography is associated with large health benefits; however, additional investment to improve mammography may not be warranted. As shown in Table 3, further improvement on this measure is expected to yield only 120,833 extra QALYs—less than 2% of the total additional QALYs that can be potentially gained from improving quality on the full set of 14 indicators. IV. SCOPE OF APPLICATION, LIMITATIONS, AND ADDITIONAL AREAS FOR FUTURE DEVELOPMENT Scope of Application A key determinant of the value of the EPV-QIR approach to selecting and/or prioritizing measures is the extent to which it is applicable across a broad range of measure types. To assess the scope of the approach, it is usual to consider several broad classes of quality indicators: Process Measures. For process measures defined explicitly on the basis of some standard of care, EVQI can be estimated as long as the net health benefit of S can be estimated using data from published studies. Composite Process Measures. The 2008 NHQR/NHDR reports on 10 composite process measures. These composites are constructed as “all-or-none” aggregates of individual process measures that measure whether an individual received all standards of care for a given condition. Individuals receiving only some of the enumerated standards are considered to have not received appropriate care, and are scored as such. The EVQI of the compos- ite requires an estimate of the NHBS associated with receiving all components of care in the composite measure. Although NHBs may be calculated for each component in the composite, one cannot sum NHBs across components

OCR for page 175
 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS TABLE 2 14 NHQR Measures Ranked in Descending Order of Value of Perfect Implementation Denominator VPI Share of Cumulative Mnemonic NHQR Measure Population (QALYs) Total VQI % VQI NHQR_DMHTN Percent of adults with diagnosed diabetes with most recent 17,268,973 7,021,537 39.33% 39.33% blood pressure <140/80 mm/Hg NHQR_DMFOOT Adults age 40+ with diagnosed diabetes who had their feet 17,268,973 2,326,165 13.03% 52.36% checked for sores or irritation in the calendar year NHQR_PAP3YR Percent of women (age 18 and over) who report they had a 15,272,448 2,120,558 11.88% 64.24% Pap smear within the past 3 yrs NHQR_DMCHOL Adults age 40 and over with diagnosed diabetes with total 17,268,973 1,828,056 10.24% 74.48% cholesterol <200 mg/dL NHQR_DMHBA1C Percent of adults with diagnosed diabetes with HbA1c level 17,268,973 1,474,394 8.26% 82.74% >9.5% (poor control); <7.0 (optimal); <9.0 (minimally acceptable) NHQR_ Percent of women (age 40+) who report they had a 60,428,554 1,167,474 6.54% 89.28% BRCA2YRMAMM mammogram within the past 2 years NHQR_HIVEVER People ages 15-44 who ever received an HIV test outside of 126,006,034 529,704 2.97% 92.25% blood donation NHQR_DMEYE Adults age 40+ with diagnosed diabetes who received a 17,268,973 414,132 2.32% 94.57% dilated eye examination in the calendar year NHQR_ Adults age 50 and over who ever received a colonoscopy, 14,992,188 366,829 2.05% 96.62% CRC50EVERCOLON sigmoidoscopy, or proctoscopy NHQR_CRCBIFOBT Adults age 50 and over who received a fecal occult blood 6,895,908 253,938 1.42% 98.04% test (FOBT) in the last 2 years NHQR_ Percent of individuals age 65+ who ever received a 38,869,716 161,291 0.90% 98.95% CAPVACC65EV pneumococcal vaccination NHQR_AMIBB Percent of AMI patients administered beta blockers 682,699 123,172 0.69% 99.64% prescribed at discharge NHQR_HFACE Percent of hospital patients with heart failure and left 295,101 64,976 0.36% 100.00% ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge NHQR_BSICVC Bloodstream infections (BSIs) per 1,000 central venous 140,000 0 0.00% 100.00% catheter (CVC) placements—CH/SSD ext TOTAL 17,852,224 100.00% to calculate the total NHB associated with the composite. The reason for this is that one cannot assume additive separability across components. There may be—for example—complementarities across components of care. Outcomes Measures. A number of intermediate- and final-outcomes measures are reported in the NHQR/ NHDR, and vary substantially in the way that they are defined. The primary problem with these measures is the lack of a specific treatment or intervention that can be identified as a target for improvement, which makes it impossible to estimate net health benefits of a standard of care, intervention, or treatment. Access/Utilization Rates. The NHQR/NHDR includes several measures defined as population utilization rates. A utilization-based measure is intended to track desirable or appropriate use of health services. These measures may be evaluated using the EVQI approach if the net health benefit for an appropriate unit of access to care can be constructed. However, if these measures are indirect measures of the failure to provide unspecified interventions or services which then, as a consequence, result in otherwise-avoidable utilization of health services, then these

OCR for page 175
 APPENDIX F TABLE 3 14 NHQR Measures Ranked in Descending Order of Value of Quality Improvement Denominator VQI Share of Cumulative Mnemonic NHQR Measure Population (QALYs) Total VQI % VQI NHQR_DMHTN Percent of adults with diagnosed diabetes with most recent 17,268,973 2,913,938 46.62% 46.62% blood pressure <140/80 mm/Hg NHQR_DMCHOL Adults age 40 and over with diagnosed diabetes with total 17,268,973 824,453 13.19% 59.81% cholesterol <200 mg/dL NHQR_DMFOOT Adults age 40+ with diagnosed diabetes who had their feet 17,268,973 681,566 10.90% 70.71% checked for sores or irritation in the calendar year NHQR_DMHBA1C Percent of adults with diagnosed diabetes with HbA1c level 17,268,973 669,375 10.71% 81.42% >9.5% (poor control); <7.0 (optimal); <9.0 (minimally acceptable) NHQR_HIVEVER People ages 15-44 who ever received an HIV test outside of 126,006,034 288,159 4.61% 86.03% blood donation NHQR_PAP3YR Percent of women (age 18 and over) who report they had a 15,272,448 216,801 3.47% 89.50% Pap smear within the past 3 yrs NHQR_DMEYE Adults age 40+ with diagnosed diabetes who received a 17,268,973 166,895 2.67% 92.17% dilated eye examination in the calendar year NHQR_ Adults age 50 and over who ever received a colonoscopy, 14,992,188 147,375 2.36% 94.53% CRC50EVERCOLON sigmoidoscopy, or proctoscopy NHQR_ Percent of women (age 40+) who report they had a 60,428,554 120,833 1.93% 96.46% BRCA2YRMAMM mammogram within the past 2 years NHQR_CRCBIFOBT Adults age 50 and over who received a fecal occult blood 6,895,908 101,441 1.62% 98.08% test (FOBT) in the last 2 years NHQR_ Percent of individuals age 65+ who ever received a 38,869,716 68,871 1.10% 99.18% CAPVACC65EVER pneumococcal vaccination NHQR_BSICVC Bloodstream infections (BSIs) per 1,000 central venous 140,000 27,809 0.44% 99.63% catheter (CVC) placements—CH/SSD (ext) NHQR_AMIBB Percent of AMI patients administered beta blockers 682,699 13,549 0.22% 99.85% prescribed at discharge NHQR_HFACE Percent of hospital patients with heart failure and left 295,101 9,616 0.15% 100.00% ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge TOTAL 349,927,514 6,250,682 100.00% suffer the same challenges as mortality-based measures and clinical intermediate outcomes measures in that net health benefits cannot be constructed. Oeruse and Inappropriate Use Measures. As noted above, the EPV-QIR approach can be extended to consider overuse. The cervical cancer screening example discussed in Appendix A provides a good example of how overuse might be addressed. As discussed in Appendix A, inappropriate use measures work similarly, with effects applied over the relevant populations in which inappropriate use is occurring. Patient Experience Measures. Finally, NHRQ/NHDR contains a number of measures of patient experience/ satisfaction. If these measures are assumed to reflect interpersonal quality of care, then the EPV-QIR approach can be applied if net health benefits can be constructed for dimensions of interpersonal relations between patients and providers. If the motivation for patient experience measures is instead driven by interest in promoting patient- centered or preference-concordant care, then estimating the EPV-QIR is more complicated. The EPV-QIR for com-

OCR for page 175
 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS As shown in Column 5, the alue of perfect implementation (VPI) is the total possible number of QALYs that can be gained by moving all women who are not currently receiving the standards of care, to biennial or annual screening. We take the average of the NHB associated with annual screening and the NHB associated with biennial screening, and then multiply this average by the number of women in each of the comparator screening categories. For women receiving annual or biennial screening, we multiply the number of women in these categories by the NHB associated with annual screening and the NHB associated with biennial screening, respectively. The VPI for the BRCA2YRMAMM measure is the sum of VPI across all screening modalities, and represents the total number of QALYs that could be achieved if all women received mammograms annual or biennially. Column 6 presents the alue of current implementation (VCI), which is the number of QALYs currently achieved given current patterns of mammography. The VCI for each screening strategy is calculated by multiply- ing the number of women in each screening category by the NHB associated with that screening strategy (see Table A.2.2). The PVCI for the BRCA2YRMAMM is the sum of PVCI across all screening modalities and repre- sents the total number of QALYs currently achieved under existing practice. Column 7 shows the maximum potential alue of quality improement, which is the difference PVPI, PVCI, and PVQI. As discussed in Part I, the maximum potential alue of quality improement represents an upper bound on the QALYs that can be achieved from improving quality of care if an intervention that was 100% effective in changing provider behavior to comply with standards of care were implemented costlessly. For the BRCA2YRMAMM measure, the maximum potential alue of quality improement (MaxPVQI) is 120,833 QALYs. A total of 1,167,474 QALYs can be achieved if all women age 40+ received annual or biennial screening beginning at age 40 and continuing for the rest of their lives. Given present patterns of screening mam - mography, 1,046,640 QALYs are being achieved. Although 82% of eligible women are receiving annual or biennial mammography, roughly 90% of total possible QALYs are being achieved. APPENDIX C Data Sources Used in EVQI Calculations Presented in This Report [NHQR_DMHTN] Percent of adults with diagnosed diabetes with most recent blood pressure <140/80 mm/Hg. Costs and Effectiveness • The CDC Diabetes Cost-Effectiveness Group. Cost-effectiveness of intensive glycemic control, inten - sified hypertension control, and serum cholesterol level reduction for Type 2 diabetes. JAMA. 2002; 287(19):2542-2551. • Note—this study ealuated an interention inoling the use of ACE-I or Beta-blocker to achiee a blood pressure of </ mmHg compared to “usual care.” Population • Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-01). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. • Centers for Disease Control and Prevention, National Center for Health Statistics, Division of Health Interview Statistics, data from the National Health Interview Survey. U.S. Bureau of the Census, census of the population and population estimates. Data computed by the Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Current Implementation Rate • NHQR 2008 (Data from 2003-6).

OCR for page 175
 APPENDIX F [NHQR_DMCHOL] Adults age 40 and over with diagnosed diabetes with total cholesterol <200 mg/dL. Costs and Effectiveness • The CDC Diabetes Cost-Effectiveness Group. Cost-effectiveness of intensive glycemic control, inten - sified hypertension control, and serum cholesterol level reduction for Type 2 diabetes. JAMA. 2002; 287(19):2542-2551. • Note—this study ealuated an interention inoling the use of Praastatin to achiee a serum choles- terol leel < 00mg/dL compared to “usual care.” Population • Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-01). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. • Centers for Disease Control and Prevention, National Center for Health Statistics, Division of Health Interview Statistics, data from the National Health Interview Survey. U.S. Bureau of the Census, census of the population and population estimates. Data computed by the Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Current Implementation Rate • NHQR 2008 (Data from 2003-6). [NHQR_DMFOOT] Adults age 40+ with diagnosed diabetes who had their feet checked for sores or irritation in the calendar year. Costs and Effectiveness • Ortegon MM, Redekop WK, Niessen LW. Cost-effectiveness of prevention and treatment of the diabetic foot: a Markov analysis. Diabetes Care. 2004;27:901-907. • Note—this study ealuated an interention inoling a program of “optimal foot care” designed to achiee a 0% reduction in foot lesions compared to “usual care.” Population • Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-01). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. • Centers for Disease Control and Prevention, National Center for Health Statistics, Division of Health Interview Statistics, data from the National Health Interview Survey. U.S. Bureau of the Census, census of the population and population estimates. Data computed by the Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Current Implementation Rate • NHQR 2008 (Data from 2005). [NHQR_DMHBA1C] Percent of adults with diagnosed diabetes with HbA1c level <9.5% (poor control); <7.0 (optimal); <9.0 (minimally acceptable). Costs and Effectiveness • The CDC Diabetes Cost-Effectiveness Group. Cost-effectiveness of intensive glycemic control, inten - sified hypertension control, and serum cholesterol level reduction for Type 2 diabetes. JAMA. 2002; 287(19):2542-2551. • Note—this study ealuated an interention inoling the use of insulin/sulfonylurea to achiee a glycemic leel < 0mg/dL or mmol/L compared to “usual care.” Population

OCR for page 175
 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS • Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-01). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. • Centers for Disease Control and Prevention, National Center for Health Statistics, Division of Health Interview Statistics, data from the National Health Interview Survey. U.S. Bureau of the Census, census of the population and population estimates. Data computed by the Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Current Implementation Rate • NHQR 2008 (Data from 2003-6). [NHQR_HIVEVER] People ages 15-44 who ever received an HIV test outside of blood donation. Costs and Effectiveness • Sanders GD, Bayoumi AM, Sundaram V, Bilir SP, Neukermans CP, Rydzak CE, Douglass LR, Lazze - roni LC, Holodniy M, Owens DK. Cost-effectiveness of screening for HIV in the era of highly active antiretroviral therapy. NEJM. 2005;352:570-85. • Note—this study ealuated HIV testing at age  in a population with a % prealence of HIV. We used estimates of the costs and health benefits accruing to the indiidual tested, and ignore costs and benefits due to spilloer to the indiidual’s partner. Population • Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-01). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. Current Implementation Rate • Kaiser State Health Facts, 2001. [NHQR_PAP3YR] Percent of women (age 18 and over) who report they had a Pap smear within the past 3 years. Costs and Effectiveness • The CDC Diabetes Cost-Effectiveness Group. Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for Type 2 diabetes. JAMA. 2002;287(19):2542-2551. • Note—this study ealuated an interention inoling the use of insulin/sulfonylurea to achiee a glycemic leel < 0mg/dL or mmol/L compared to “usual care.” Population • Table 2: Annual Estimates of the Resident Population by Sex and Selected Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-02). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. Current Implementation Rate • BRFSS 2005 [NHQR_DMEYE] Adults age 40+ with diagnosed diabetes who received a dilated eye examination in the calendar year. Costs and Effectiveness • Vijan S, Hofer TP, Hayward RA. Cost-utility analysis of screening intervals for diabetic retinopathy in patients with Type 2 diabetes mellitus. JAMA. 2000;283(7):889-896.

OCR for page 175
 APPENDIX F Population • Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-01). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. • Centers for Disease Control and Prevention, National Center for Health Statistics, Division of Health Interview Statistics, data from the National Health Interview Survey. U.S. Bureau of the Census, census of the population and population estimates. Data computed by the Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Current Implementation Rate • NHQR 2008 (Data from 2005). [NHQR_CRC50EVERCOLON] Adults age 50 and over who ever received a colonoscopy, sigmoidoscopy, or proctoscopy. Costs and Effectiveness • Frazier AL, Colditz GA, Fuchs CS et al. Cost-effectiveness of screening for colorectal cancer in the general population. JAMA. 2000;284(15):1954-1961. • Note—this study ealuated annual FOBT in a population representatie of the population of adults age 0+ in the U.S., but the study data come from white males age 0+. • Note—this study ealuated one-time colonoscopy at age . Population • Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-01). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. Current Implementation Rate • BRFSS 2005. [NHQR_BRCA2YRMAMM] Percent of women (age 40+) who report they had a mammogram within the past 2 years. Costs and Effectiveness • Stout NK, Rosenberg MA, Trentham-Dietz A, Smith MA, Robinson SM, Fryback DG. Retrospective cost-effectiveness analysis of screening mammography. J Natl Cancer Inst. 2006;98(11):774-82. • Note—this study ealuated biennial screening beginning at age 0 and ending at age 0. Population • Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-01). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. Current Implementation Rate • BRFSS 2005. [NHQR_CRCBIFOBT] Adults age 50 and over who received a fecal occult blood test (FOBT) in the last 2 years. Costs and Effectiveness • Frazier AL, Colditz GA, Fuchs CS et al. Cost-effectiveness of screening for colorectal cancer in the general population. JAMA. 2000;284(15):1954-1961. • Note—this study ealuated annual FOBT in a population representatie of the population of adults age 0+ in the U.S., but the study data come from white males age 0+.

OCR for page 175
00 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS • Note—this study ealuated two types of FOBT—rehydrated FOBT (RFOBT) and unrehydrated FOBT (UFOBT). We used an aeraged the NHBs associated with RFOBT and UFOBT in our calculations. Population • Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-01). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. Current Implementation Rate • BRFSS 2005. [NHQR_CAPVACC65EVER] Percent of individuals age 65+ who ever received a pneumococcal vaccination. Costs and Effectiveness • Sisk JE, Moskowitz AJ, Whang W, Lin JD, Fedson DS, McBean AM, Plouffe JF, Cetron MS, Butler JC. Cost-effectiveness of vaccination against pneumococcal bacteremia among elderly people. JAMA. 1997;278:1333-1339. Population • Table 2: Annual Estimates of the Resident Population by Sex and Selected Age Groups for the United States: April 1, 2000 to July 1, 2008 (NC-EST2008-02). Source: Source: Population Division, U.S. Census Bureau. Release Date: May 14, 2009. Current Implementation Rate • NHQR 2008 (2006). [NHQR_BSICVC] Bloodstream infections (BSIs) per 1,000 central venous catheter (CVC) placements. Costs and Effectiveness • Halton KA, Cook DA, Whitby M, Paterson DL, Graves N. Cost-effectiveness of antimicrobial catheters in the intensive care unit: addressing uncertainty in the decision. Critical Care. 2009;13(2):R35. • Note—costs were presented in 00 Australian Dollars. We conerted costs to 00 U.S. Dollars using a historical currency exchange table, and then adjusted costs to 00 U.S. Dollars using the Consumer Price Index. Population • Maki DG, Stolz SM, Wheeler S, Mermel LA. Prevention of central venous catheter-related bloodstream infection by use of an antiseptic-impregnated catheter: a randomized, controlled trial. Annals of Internal Medicine. 1997;127(4): 257-66. Current Implementation Rate • NHQR 2008 (2004). [NHQR_AMIBB] Percent of AMI patients administered beta blockers prescribed at discharge. Costs and Effectiveness • Phillips KA, Shlipak MG, Coxson P, Heidenreich PA, Hunink M, Goldman PA, Williams LW, Wein- stein MC, Goldman. Health and economic benefits of increased beta-blocker use following myocardial infarction. JAMA. 2000;284:2748-2754. Population • 2005 National Hospital Discharge Survey. Available at: www.cdc.gov. • Population-weighted estimates of hospital discharges with 3-digit ICD-9-CM code = 410 as primary diagnosis. Current Implementation Rate • NHQR 2008 (2004).

OCR for page 175
0 APPENDIX F [NHQR_HFACE] Percent of hospital patients with heart failure and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge. Costs and Effectiveness • Boyko WL Jr, Glick HA, Schulman KA. Economics and cost-effectiveness in evaluating the value of cardiovascular therapies. ACE inhibitors in the management of congestive heart failure: comparative economic data. Am Heart J. 1999;137(5):S115-9. Population • 2005 National Hospital Discharge Survey. Available at: www.cdc.gov. • Population-weighted estimates of hospital discharges with 3-digit ICD-9-CM code = 428 as primary diagnosis. • Prevalence of LVSD among patients with HF: Senior R, Galasko G. Cost-effective strategies to screen for left ventricular systolic dysfunction in the community—a concept. Congestie Heart Failure. 2007;11(4):194-211. Also see: • Kelly R, Staines A, MacWalter R, Stonebridge P, Tunstall-Pedoe H, Struthers AD. The prevalence of treatable left ventricular systolic dysfunction in patients who present with noncardiac vascular episodes: a case-control study. J Am Coll Cardiol. 2002;39(2):219-24. Current Implementation Rate • NHQR 2008 (2004). [NHQR_AMIACE] Percent of hospital patients with heart failure and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge. Costs and Effectiveness • Tsevat J, Duke D, Goldman L, Pfeffer MA, Lamas GA, Soukup JR, Kuntz KM, Lee TH. Cost- effectiveness of captopril therapy after myocardial infarction. JACC. 1995;26(4):914-919. Population • 2005 National Hospital Discharge Survey. Available at: www.cdc.gov. • Population-weighted estimates of hospital discharges with 3-digit ICD-9-CM code = 410 as primary diagnosis. • Prevalence of LVSD among patients with AMI—we use an estimate of 27.2% in all patients hospitalized patients with AMI. This estimate was taken from the SOLVD Trial, as summarized in: Weir R McMur- ray JJ, Velazquez EJ. Epidemiology of heart failure and left ventricular systolic dysfunction after acute myocardial infarction: prevalence, clinical characteristics, and prognostic importance. Am J Cardiol. 2006;97[suppl]:13F-25F. Current Implementation Rate • NHQR 2008 (2005-2006, all payers). CALCULATION 3. Outcomes-Based Quality Measure—Standard of Care Not Specified. Bloodstream Infections (BSIs) per 1,000 Central Venous Catheter (CVC) Placements [NHQR BSICVC] Standard of Care. This is an outcomes measure that tracks an adverse event during hospitalization, namely, bloodstream infection resulting from a central venous catheter. The standard of care is not defined. There are multiple processes of care that can reduce bloodstream infections associated with central venous catheter place - ments: for example, hand-washing, skin cleaning, and the use of antimicrobial dressing and antimicrobial-coated catheters. Because cost-effectiveness studies have investigated the use of coated catheters in reducing CVC-related BSIs, we use coated catheters as our standard of care in calculating the value of quality improvement with respect to reducing the number of CVC-related BSIs. Number of Indiiduals Receiing Standard of Care and Non-Standard Care. We could not find data on the number of catheter placements in the U.S., so for our calculations, we used an estimate of the number of CVCs

OCR for page 175
0 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS sold in the U.S. each year from Maki et al. (1997). This is likely to be an overestimate of the number of CVC placements in the U.S. Based on infection rates published in the 2008 NHQR, there were approximately 140,000 CVC-related BSIs in the U.S. (3% infection rate applied to a base denominator of 5 million catheters). We make an implicit assumption that an infection rate of zero is possible and desirable. Table A.3.1 shows our estimates of the denominator population and number of infections for the CVCBSI measure. TABLE A.3.1 Number of Individuals Receiving Non-standard Care: CVCBSI Parameter Source Base Population 5,000,000 # of CVCs sold annually in U.S. Maki et al. (1997) Current Infection Rate 3% NHQR 2008 (2006) Rate of BSI in CVCs N Infections 140,000 SOURCE: Maki DG, Stolz SM, Wheeler S, Mermel LA. Prevention of central venous catheter-related bloodstream infection by use of an antiseptic-impregnated catheter: a randomized, controlled trial. Annals of Internal Medicine. 1997;127(4): 257-66. NHQR 2008. Calculation of Net Health Benefit. We use data from Halton et al. (2009) that compares the costs and effec- tiveness of various antimicrobial catheters compared to uncoated catheters in preventing infections. Although this study was conducted in Australia (and focused on the cost-effectiveness of antimicrobial catheters in intensive care units), this was the only study with usable, published estimates of costs and effectiveness in QALYs for uncoated catheters (comparator) as well as coated catheters (standard of care). Halton et al. evaluated four differ - ent types of coated CVCs relative to uncoated CVCs: chlorhexidine/silver sulfadiazine externally coated catheters; chlorhexidine/silver sulfadiazine internally and externally coated catheters; silver, platinum, and carbon-coated catheters; and minocycline and rifampicin-coated catheters. We calculated the NHB of each type of catheter rela- tive to uncoated catheters. Table A.3.2 presents the components of the NHB calculation for the CVCBSCI measure for each catheter type, as shown in Column 1. This study only published the incremental cost and the incremental effectiveness of each coated catheter relative to uncoated catheters. Columns 2 and 3 present these numbers. We again assume a value of $100,000 for the cost-effectiveness threshold (Column 4). Column 5 presents our calculation of the NHB given incremental costs and effectiveness published in the study. Column 6 presents data from Halton et al. (2009) on the number of infections that use of each catheter type can prevent. We divided the NHB (Column 5) by the number of infections avoided (Column 6) to estimate the net health benefit per infection avoided, as shown in Column 7. TABLE A.3.2 Calculation of Net Health Benefits: CVCBSI l Incr. Effect’ness Net Health N Infect. NHB QALYs per Care Type Cost† in QALYs† ($/QALY) Benefit in QALYs Avoid’d Infect. Avoid’d Chlorhexidine silver sulfadiazine catheters –75,856 0.91000 100,000 1.66856 8 0.19864 (external coat)* Chlorhexidine silver sulfadiazine catheters –41,576 0.80000 100,000 1.21576 7 0.16429 (internal+external coat)* Silver, platinum and carbon catheters* –97,634 1.23000 100,000 2.20634 11 0.19354 Minocycline and rifampicin coated catheters* –105,951 1.64000 100,000 2.69951 15 0.17760 Uncoated central venous catheter (Baseline) 100,000 † Incremental cost and effectiveness are relative to baseline care type of uncoated central venous catheter utilization. * Care Type compliant with quality measure. SOURCE: Halton KA, Cook DA, Whitby M, Paterson DL, Graves N. Cost-effectiveness of antimicrobial catheters in the intensive care unit: addressing uncertainty in the decision. Critical Care. 2009;13(2):R35. EPV-QIR Calculations. Our EVQI calculations for the NHQR_CVCBSCI measure are shown in Table C.3. Again, each catheter type is displayed in Column 1 of Table A.3.3. We reason that NHBs gained per infection

OCR for page 175
0 APPENDIX F avoided, can also be viewed as the net health benefit lost for every infection that occurred. Thus, Column 2 pres- ents the NHBs lost per infection under different catheter “regimes.” Column 3 reports the NHB lost per infection under perfect implementation. This column is zero because under perfect implementation, we assume that there would be no bloodstream infections associated with central venous catheters. Column 4 reports the number of infections, which is 140,000 (Table C.1). The value of perfect implementation is zero, because this measure per- tains to an adverse outcome that should not occur under perfect implementation. Thus, the only “gains” are losses averted. These losses are shown in Column 5. Compared to a regime where externally coated chlorhexidine silver sulfadiazine catheters are used exclusively, current implementation results in a loss of 27,809 QALYs. Compared to a regime in which minocycline and rifampicin-coated catheters are used exclusively, current implementation results in a loss of 24,864 QALYs. The value of quality improvement—which, in our analysis, implies switch - ing regimes from uncoated catheters to a coated catheter, is equal to the absolute value of the QALYs currently lost. For example, the maximum alue of quality improement resulting from a switch from uncoated catheters to chlorhexidine silver sulfadiazine externally coated catheters is 27,809 QALYs. TABLE A.3.3 The Value of Perfect and Current Implementation, and Quality Improvement: CVCBSI NHB QALYs Population Population Maximum NHB QALYs (Lost) per Infection Value of Perfect Value of Current Population Value of (Lost) per Under Perfect Imp. N Implement’n Implement’n Quality Improvem’t Care Type Infection QALYs Infections (VPI) QALYs (VCI) QALYs QALYs Chlorhexidine silver sulfadiazine –0.19864 0.00000 140,000 0 –27,809 27,809 catheters (external coat)* Chlorhexidine silver sulfadiazine –0.16429 0.00000 140,000 0 –23,001 23,001 catheters (internal+external coat)* Silver, platinum and carbon –0.19354 0.00000 140,000 0 –27,095 27,095 catheters* Minocycline and rifampicin coated –0.17760 0.00000 140,000 0 –24,864 24,864 catheters* Uncoated central venous catheter 140,000 (Baseline) * Care Type compliant with quality measure. CALCULATION 4. Complex Denominator Populations. Percent of hospital patients with heart attack and left ventricular systolic dysfunction who were prescribed angiotensin converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) at discharge [AMIACE] Standard of Care. The standard of care in this measure is the receipt of a prescription for ACE/ARB at dis - charge among patients hospitalized for acute myocardial infarction (AMI). Number of Indiiduals Eligible for the Standard of Care. We used the 2005 National Hospital Discharge Survey to obtain population-weighted estimates of the number of hospital discharges in the U.S. in 2005 that had a primary diagnosis of acute myocardial infarction (AMI),6 by age group. Age groups were defined on the basis of the cost-effectiveness study from which we obtain our estimates of costs and effectiveness, as we describe in the following section. Because the AMIACE measure applies only to hospital discharges with heart failure and left ventricular systolic dysfunction (LVSD), we assumed an LVSD prevalence of 27% among patients with AMI also based on data from the Valsartan in Acute Myocardial Infarction Trial (VALIANT) (Weir et al. 2006). According to the 2008 NHQR, the current rate of implementation for ACE/ARB at discharge for patients with AMI and LVSD is 86% in the overall population of patients with AMI and LVSD. Rates stratified by age group are not reported. Thus, we made the assumption that current implementation rates did not differ by age group. Table A.4.1 reports our estimates of the number of patients receiving the standard of care in each age group. 6 We identified hospital discharges with AMI based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD- 9-CM) code recorded under primary diagnosis (ICD-9-CM = 410).

OCR for page 175
0 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS TABLE A.4.1 Number of Individuals Receiving Standard and Non-standard Care: AMIACE Parameter Source Base Population—Age < 60 51,054 NHDS 2005 (AMI Discharges, age < 60), 27% LVSD Base Population—60 ≤ Age < 70 NHDS 2005 (AMI Discharges, 60 ≤ age < 70), 27% LVSD 39,171 Base Population 70 ≤ Age NHDS 2005 (AMI Discharges, age ≥ 70) 27% LVSD 95,470 Current Implementation Rate 86% NHQR (2008) (2003-2006, All Payers) N Receiving Standard of Care—Age < 60 43,855 N Receiving Standard of Care—60 ≤ Age < 70 33,648 N Receiving Standard of Care—Age ≥ 70 82,008 N NOT Receiving Standard of Care—Age < 60 7,199 N NOT Receiving Standard of Care—60 ≤ Age < 70 5,523 N NOT Receiving Standard of Care—Age ≥ 70 13,461 SOURCE: 2005 National Hospital Discharge Survey. Weir RAP, McMurray JJV, Velazquez EJ. Epidemiology of heart failure and left ventricular systolic dysfunction after acute myocardial infarc- tion: prevalence, clinical characteristics and prognostic importance. American Journal of Cardiology. 2006;97[suppl]:13F-25F). Calculation of Net Health Benefit. Data on the costs and effectiveness of ACE/ARB come from a cost- effectiveness study by Tsevat et al. (1995) on the use of Captopril (an ACE inhibitor) among survivors of AMI in three age groups: 50-60, 60-70, 70-80, and 80+ year-olds. We used figures from the “limited-benefit” model estimated by Tsevat et al., which assumes that ACE-I does not confer survival benefits beyond 4 years post-AMI. Table A.4.2 provides the inputs and final NHB calculations for ACE therapy in each age group (Column 1). Col- umns 2 and 3 report the costs and effectiveness of ACE-I and no ACE-I in each age group, and Columns 4 and 5 report the incremental costs and effectiveness. Note the much larger incremental difference in the effectiveness of ACE-I in the oldest age group compared to the youngest age group. TABLE A.4.2 Calculation of Net Health Benefits: AMIACE l Cost per Person Outcomes Incr. Effect’ness† Net Health Care Type in 2009 $USD (QALYs per Person) Cost† in QALYs ($/QALY) Benefit in QALYs Age 50 No Captopril 47,983 8.10000 100,000 Captopril* 50,715 8.13000 2,732 0.03000 100,000 0.00268 Age 60 No Captopril 38,629 6.33000 100,000 Captopril* 41,282 6.51000 2,653 0.18000 100,000 0.15347 Age 70 No Captopril 30,176 4.72000 100,000 Captopril* 32,899 5.07000 2,722 0.35000 100,000 0.32278 † Incremental cost and effectiveness are relative to baseline care type of no Captopril (no ACE/ARB). * Care Type compliant with quality measure. SOURCE: Tsevat J, Duke D, Goldman L, Pfeffer MA, Lamas GA, Soukup JR, Kuntz KM, Lee TH. Cost-effectiveness of captopril therapy after myocardial infarction. Journal of the American College of Cardiology. 1995;26(4):914-19. Table A.4.3 shows the final EVQI calculations for ACEAMI. For each age group in Column 1, Column 2 of Table A.4.3 shows the NHB of ACE-I after AMI in that age group. Column 4 shows the number of patients in each age group who currently receive ACE-I, and the number who do not currently receive ACE-I after AMI. The value of perfect implementation is reported in Column 5, and represents the maximum NHB that would be obtained if all patients in each age group received ACE-I after AMI. Column 6 shows the value of current implementation, given extant rates of prescribing ACE-I at discharge. Column 7 shows the maximum potential NHB that can be gained from improving ACEAMI to 100% from current levels of implementation.

OCR for page 175
0 APPENDIX F TABLE A.4.3 The Value of Perfect and Current Implementation, and Quality Improvement: AMIACE Net Health NHB under Population Value of Population Value of Maximum Population Benefit Perfect Imp. N Perfect Implement’n Current Implement’n Value of Quality Care Type QALYs QALYs Persons (VPI) QALYs (VCI) QALYs Improvem’t QALYs Age 50 No Captopril 0.00268 7,199 19 0 19 Captopril* 0.00268 0.00268 43,855 118 118 0 Age 60 No Captopril 0.15347 5,523 848 0 848 Captopril* 0.15347 0.15347 33,648 5,164 5,164 0 Age 70 No Captopril 0.32278 13,461 4,345 0 4,345 Captopril* 0.32278 0.32278 82,008 26,470 26,470 0 Total 185,694 36,964 31,752 5,212 * Care Type compliant with quality measure.

OCR for page 175