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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
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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 indicators 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:
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,
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,
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
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 improvement 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 calculating 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 standard 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
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S can be written as ∆e = 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 ∆c = cS – cO.
Net Health Benefit (NHB). The net health benefit of the standard of care (NHBS) relative to O, is the incremental 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):
[Eq. 1]
In Eq. 1, λ 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, λ 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 ∆c/λ 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, ∆c/λ 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) <λ , the NHB will be positive. Conversely, the NHB will be negative when an intervention is not cost-effective, because the opportunity cost of the intervention will exceed its health benefits.
Because the NHB depends on how opportunity costs are valued in terms of health, NHB depends on the level used for λ. Thresholds of $50,000 and $100,000 per QALY have been commonly used in cost-effectiveness studies, but no universally accepted reference value for λ exists (Hirth et al., 2000). More recent literature has scrutinized the validity of these traditional threshold values and general failure to adjust the threshold for inflation (Ubel et al., 2003). Studies have suggested threshold values of: $109,000-$297,000 USD2003 per QALY (Braithwaite et al., 2008); $12,500-$32,200 USD2003 per QALY (King et al., 2005); $24,777-$428,286 USD1997 per QALY (Hirth et al., 2000). Because the net health benefit framework is sensitive to the value used for λ, the NHB is traditionally reported over a broad range of values of λ.
Population Value of Perfect Implementation (PVPI). A standard of care should generally be implemented when its expected benefits exceed its expected risks. We define the number of individuals, NS, in a population who should receive the standard of care as the measure population. Assuming that individuals outside the measure population do not receive the care, perfect implementation occurs when all individuals in the measure population receive the standard of care. The population value of perfect implementation (PVPI) is the total NHB achieved in the measure population when the standard of care is applied to every patient in the measure population. PVPI is calculated by multiplying the total number of individuals in the measure population (NS) by the net health benefit of S (NHBS):
[Eq. 2]
Population Value of Current Implementation (PVCI). Under perfect implementation, all individuals in a measure population receive the standard of care. When a standard is “underused,” the rate of current implementation, rSC, is less than 100%. The population value of current implementation (PVCI) is the total net health benefits achieved from the health intervention given current implementation rates:
[Eq. 3]
When performance is perfect, every eligible individual in the population receives the standard of care, so PVPI = PVCI, and no further net health benefits can be gained from improving performance.
Maximum Population Value of Quality Improvement (MaxPVQI). Quality effort improvements can be thought of as interventions to perfect implementation. The maximum population value of quality improvement (MaxPVQI)
1
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.
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is the total net health benefits that can be gained by improving implementation from current rates to 100%. MaxPVQI is simply the difference between PVPI and PVCI, or
[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 Overuse. 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 implemented 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 Improvement (EPV-QI). The MaxPVQI assumes that both the current rate of implementation is known and that quality improvement results in 100% implementation. The expected population value of quality improvement (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 and be the rates of implementation before and after some QI initiative so that is the change in implementation before and after the intervention. Because these elements and their change can be uncertain, we reflect this uncertainty by assuming the change in implementation with a quality improvement effort is distributed so that the expected extent of quality improvement would be and the expected population value of quality improvement would be:
[Eq. 5]
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 . We denote this gain in implementation with quality reporting as , and for simplicity assume that the uncertainty in how reporting will effect quality can be represented by a probability of undertaking quality improvement action, πQI, so that is the expected change in implementation with quality reporting and the expected population value of quality reporting is:
[Eq. 6]
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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.
This equation provides our fundamental framework for developing estimates of the value of quality reporting 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 quality improvement action with probability πQ. The effectiveness of a quality improvement action is the effect size of that action, or ∆rSC. The population value 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 value of quality improvement (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.
FIGURE 2 Conceptual Summary of the EPV-QIR Approach
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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 implementing 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 comparator 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 Individuals 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,” .
This data would typically be available for existing quality measures that had previously been collected, allowing 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 value of perfect implementation (PVPI) might be the most informative bound possible.
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Expected Quality Improvements. To develop more precise estimates of the EPV-QIR, it is necessary to know the probability of quality improvement (πQ) and the effect size of quality improvement interventions (∆rSC).2
Probability of Quality Improvement (πQ). One key factor is how providers might approach quality improvement 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 πQ 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.
Effectiveness of Quality Improvement (∆rSC). 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 improvement 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 current 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 population value 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 value 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.
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TABLE 1 EPV-QIR Calculations for 18 NHQR Measures
Mnemonic
NHQR Measure
Denominator Population
Pop. VPI (QALYs)
Pop. VCI (QALYs)
Pop. Max VQI (QALYs)
NHQR_DMHTN
Percent of adults with diagnosed diabetes with most recent blood pressure <140/80 mm/Hg
17,268,973
7,021,537
4,107,599
2,913,938
NHQR_DMCHOL
Adults age 40 and over with diagnosed diabetes with total cholesterol <200 mg/dL
17,268,973
1,828,056
1,003,602
824,453
NHQR_DMFOOT
Adults age 40+ with diagnosed diabetes who had their feet checked for sores or irritation in the calendar year
17,268,973
2,326,165
1,644,599
681,566
NHQR_DMHBA1C
Percent of adults with diagnosed diabetes with HbA1c level >9.5% (poor control); <7.0 (optimal); <9.0 (minimally acceptable)
17,268,973
1,474,394
805,019
669,375
NHQR_HIVEVER
People ages 15-44 who ever received an HIV test outside of blood donation
126,006,034
529,704
241,545
288,159
NHQR_PAP3YR
Percent of women (age 18 and over) who report they had a Pap smear within the past 3 yrs
15,272,448
2,120,558
1,903,757
216,801
NHQR_DMEYE
Adults age 40+ with diagnosed diabetes who received a dilated eye examination in the calendar year
17,268,973
414,132
247,237
166,895
NHQR_CRC50EVERCOLON
Adults age 50 and over who ever received a colonoscopy, sigmoidoscopy, or proctoscopy
14,992,188
366,829
219,454
147,375
NHQR_BRCA2YRMAMM
Percent of women (age 40+) who report they had a mammogram within the past 2 years
60,428,554
1,167,474
1,046,640
120,833
NHQR_CRCBIFOBT
Adults age 50 and over who received a fecal occult blood test (FOBT) in the last 2 years
6,895,908
253,938
152,497
101,441
NHQR_CAPVACC65EVER
Percent of individuals age 65+ who ever received a pneumococcal vaccination
38,869,716
161,291
92,420
68,871
NHQR_BSICVC
VQI represents QALYs that can be saved by using chlorhexidine silver sulfadiazine coated catheters (external coat)
Bloodstream infections (BSIs) per 1,000 central venous catheter (CVC) placements
140,000
0
–27,809
27,809
NHQR_BSICVC
VQI represents QALYs that can be saved by using silver, platinum and carbon coated catheters
Bloodstream infections (BSIs) per 1,000 central venous catheter (CVC) placements
140,000
0
−27,095
27,095
NHQR_BSICVC
VQI represents QALYs that can be saved by using chlorhexidine minocycline and rifampicin coated catheters
Bloodstream infections (BSIs) per 1,000 central venous catheter (CVC) placements
140,000
0
–24,864
24,864
NHQR_BSICVC
VQI represents QALYs that can be saved by using chlorhexidine silver sulfadiazine coated catheters (internal + external coat)
Bloodstream infections (BSIs) per 1,000 central venous catheter (CVC) placements
140,000
0
–23,001
23,001
NHQR_AMIBB
Percent of AMI patients administered beta blockers prescribed at discharge
682,699
123,172
109,623
13,549
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Mnemonic
NHQR Measure
Denominator Population
Pop. VPI (QALYs)
Pop. VCI (QALYs)
Pop. Max VQI (QALYs)
NHQR_HFACE
Percent of hospital patients with heart failure and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge
295,101
64,976
55,359
9,616
NHQR_AMIACE
Percent of AMI patients with LVSD prescribed ACE inhibitor at discharge
185,695
44,830
38,509
6,321
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 value of quality improvement (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 implementation 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 complementary 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 composite 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
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TABLE 2 14 NHQR Measures Ranked in Descending Order of Value of Perfect Implementation
Mnemonic
NHQR Measure
Denominator Population
VPI (QALYs)
Share of Total VQI
Cumulative % VQI
NHQR_DMHTN
Percent of adults with diagnosed diabetes with most recent blood pressure <140/80 mm/Hg
17,268,973
7,021,537
39.33%
39.33%
NHQR_DMFOOT
Adults age 40+ with diagnosed diabetes who had their feet checked for sores or irritation in the calendar year
17,268,973
2,326,165
13.03%
52.36%
NHQR_PAP3YR
Percent of women (age 18 and over) who report they had a Pap smear within the past 3 yrs
15,272,448
2,120,558
11.88%
64.24%
NHQR_DMCHOL
Adults age 40 and over with diagnosed diabetes with total cholesterol <200 mg/dL
17,268,973
1,828,056
10.24%
74.48%
NHQR_DMHBA1C
Percent of adults with diagnosed diabetes with HbA1c level >9.5% (poor control); <7.0 (optimal); <9.0 (minimally acceptable)
17,268,973
1,474,394
8.26%
82.74%
NHQR_ BRCA2YRMAMM
Percent of women (age 40+) who report they had a mammogram within the past 2 years
60,428,554
1,167,474
6.54%
89.28%
NHQR_HIVEVER
People ages 15-44 who ever received an HIV test outside of blood donation
126,006,034
529,704
2.97%
92.25%
NHQR_DMEYE
Adults age 40+ with diagnosed diabetes who received a dilated eye examination in the calendar year
17,268,973
414,132
2.32%
94.57%
NHQR_ CRC50EVERCOLON
Adults age 50 and over who ever received a colonoscopy, sigmoidoscopy, or proctoscopy
14,992,188
366,829
2.05%
96.62%
NHQR_CRCBIFOBT
Adults age 50 and over who received a fecal occult blood test (FOBT) in the last 2 years
6,895,908
253,938
1.42%
98.04%
NHQR_ CAPVACC65EV
Percent of individuals age 65+ who ever received a pneumococcal vaccination
38,869,716
161,291
0.90%
98.95%
NHQR_AMIBB
Percent of AMI patients administered beta blockers prescribed at discharge
682,699
123,172
0.69%
99.64%
NHQR_HFACE
Percent of hospital patients with heart failure and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge
295,101
64,976
0.36%
100.00%
NHQR_BSICVC
Bloodstream infections (BSIs) per 1,000 central venous catheter (CVC) placements—CH/SSD ext
140,000
0
0.00%
100.00%
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
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TABLE 3 14 NHQR Measures Ranked in Descending Order of Value of Quality Improvement
Mnemonic
NHQR Measure
Denominator Population
VQI (QALYs)
Share of Total VQI
Cumulative % VQI
NHQR_DMHTN
Percent of adults with diagnosed diabetes with most recent blood pressure <140/80 mm/Hg
17,268,973
2,913,938
46.62%
46.62%
NHQR_DMCHOL
Adults age 40 and over with diagnosed diabetes with total cholesterol <200 mg/dL
17,268,973
824,453
13.19%
59.81%
NHQR_DMFOOT
Adults age 40+ with diagnosed diabetes who had their feet checked for sores or irritation in the calendar year
17,268,973
681,566
10.90%
70.71%
NHQR_DMHBA1C
Percent of adults with diagnosed diabetes with HbA1c level >9.5% (poor control); <7.0 (optimal); <9.0 (minimally acceptable)
17,268,973
669,375
10.71%
81.42%
NHQR_HIVEVER
People ages 15-44 who ever received an HIV test outside of blood donation
126,006,034
288,159
4.61%
86.03%
NHQR_PAP3YR
Percent of women (age 18 and over) who report they had a Pap smear within the past 3 yrs
15,272,448
216,801
3.47%
89.50%
NHQR_DMEYE
Adults age 40+ with diagnosed diabetes who received a dilated eye examination in the calendar year
17,268,973
166,895
2.67%
92.17%
NHQR_ CRC50EVERCOLON
Adults age 50 and over who ever received a colonoscopy, sigmoidoscopy, or proctoscopy
14,992,188
147,375
2.36%
94.53%
NHQR_ BRCA2YRMAMM
Percent of women (age 40+) who report they had a mammogram within the past 2 years
60,428,554
120,833
1.93%
96.46%
NHQR_CRCBIFOBT
Adults age 50 and over who received a fecal occult blood test (FOBT) in the last 2 years
6,895,908
101,441
1.62%
98.08%
NHQR_ CAPVACC65EVER
Percent of individuals age 65+ who ever received a pneumococcal vaccination
38,869,716
68,871
1.10%
99.18%
NHQR_BSICVC
Bloodstream infections (BSIs) per 1,000 central venous catheter (CVC) placements—CH/SSD (ext)
140,000
27,809
0.44%
99.63%
NHQR_AMIBB
Percent of AMI patients administered beta blockers prescribed at discharge
682,699
13,549
0.22%
99.85%
NHQR_HFACE
Percent of hospital patients with heart failure and left ventricular systolic dysfunction who were prescribed ACE inhibitor or ARB at discharge
295,101
9,616
0.15%
100.00%
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.
Overuse 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-
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As shown in Column 5, the value 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 value 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 multiplying 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 represents the total number of QALYs currently achieved under existing practice.
Column 7 shows the maximum potential value of quality improvement, which is the difference PVPI, PVCI, and PVQI. As discussed in Part I, the maximum potential value of quality improvement 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 value of quality improvement (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 mammography, 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, intensified hypertension control, and serum cholesterol level reduction for Type 2 diabetes. JAMA. 2002; 287(19):2542-2551.
Note—this study evaluated an intervention involving the use of ACE-I or Beta-blocker to achieve a blood pressure of <144/82 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).
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[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, intensified hypertension control, and serum cholesterol level reduction for Type 2 diabetes. JAMA. 2002; 287(19):2542-2551.
Note—this study evaluated an intervention involving the use of Pravastatin to achieve a serum cholesterol level < 200mg/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 evaluated an intervention involving a program of “optimal foot care” designed to achieve a 90% 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, intensified hypertension control, and serum cholesterol level reduction for Type 2 diabetes. JAMA. 2002; 287(19):2542-2551.
Note—this study evaluated an intervention involving the use of insulin/sulfonylurea to achieve a glycemic level < 108mg/dL or 6mmol/L compared to “usual care.”
Population
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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, Lazzeroni 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 evaluated HIV testing at age 43 in a population with a 1% prevalence of HIV. We used estimates of the costs and health benefits accruing to the individual tested, and ignore costs and benefits due to spillover to the individual’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 evaluated an intervention involving the use of insulin/sulfonylurea to achieve a glycemic level < 108mg/dL or 6mmol/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.
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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 evaluated annual FOBT in a population representative of the population of adults age 50+ in the U.S., but the study data come from white males age 50+.
Note—this study evaluated one-time colonoscopy at age 55.
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 evaluated biennial screening beginning at age 40 and ending at age 80.
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 evaluated annual FOBT in a population representative of the population of adults age 50+ in the U.S., but the study data come from white males age 50+.
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Note—this study evaluated two types of FOBT—rehydrated FOBT (RFOBT) and unrehydrated FOBT (UFOBT). We used an averaged 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 2006 Australian Dollars. We converted costs to 2006 U.S. Dollars using a historical currency exchange table, and then adjusted costs to 2009 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, Weinstein 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).
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[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. Congestive 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 McMurray 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 placements: 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 Individuals Receiving 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
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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 effectiveness 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 different 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 relative 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 $100,000 for the cost-effectiveness threshold (Column 4). Column 5 presents our calculation of the NHB given incremental costs and effectiveness published in the study. Column 6 presents data from Halton et al. (2009) on the number of infections that use of each catheter type can prevent. We divided the NHB (Column 5) by the number of infections avoided (Column 6) to estimate the net health benefit per infection avoided, as shown in Column 7.
TABLE A.3.2 Calculation of Net Health Benefits: CVCBSI
Care Type
Incr. Cost†
Effect’ness in QALYs†
λ ($/QALY)
Net Health Benefit in QALYs
N Infect. Avoid’d
NHB QALYs per Infect. Avoid’d
Chlorhexidine silver sulfadiazine catheters (external coat)*
–75,856
0.91000
100,000
1.66856
8
0.19864
Chlorhexidine silver sulfadiazine catheters (internal+external coat)*
−41,576
0.80000
100,000
1.21576
7
0.16429
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
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avoided, can also be viewed as the net health benefit lost for every infection that occurred. Thus, Column 2 presents 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 pertains 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 switching regimes from uncoated catheters to a coated catheter, is equal to the absolute value of the QALYs currently lost. For example, the maximum value of quality improvement 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
Care Type
NHB QALYs (Lost) per Infection
NHB QALYs (Lost) per Infection Under Perfect Imp. QALYs
N Infections
Population Value of Perfect Implement’n (VPI) QALYs
Population Value of Current Implement’n (VCI) QALYs
Maximum Population Value of Quality Improvem’t QALYs
Chlorhexidine silver sulfadiazine catheters (external coat)*
−0.19864
0.00000
140,000
0
−27,809
27,809
Chlorhexidine silver sulfadiazine catheters (internal+external coat)*
−0.16429
0.00000
140,000
0
−23,001
23,001
Silver, platinum and carbon catheters*
−0.19354
0.00000
140,000
0
−27,095
27,095
Minocycline and rifampicin coated catheters*
−0.17760
0.00000
140,000
0
−24,864
24,864
Uncoated central venous catheter (Baseline)
140,000
* 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 discharge among patients hospitalized for acute myocardial infarction (AMI).
Number of Individuals 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).
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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
39,171
NHDS 2005 (AMI Discharges, 60 age < 70), 27% LVSD
Base Population 70 ≤ Age
95,470
NHDS 2005 (AMI Discharges, age 70) 27% LVSD
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 infarction: 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). Columns 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
Care Type
Cost per Person in 2009 $USD
Outcomes (QALYs per Person)
Incr. Cost†
Effect’ness† in QALYs
λ ($/QALY)
Net Health 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.
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TABLE A.4.3 The Value of Perfect and Current Implementation, and Quality Improvement: AMIACE
Care Type
Net Health Benefit QALYs
NHB under Perfect Imp. QALYs
N Persons
Population Value of Perfect Implement’n (VPI) QALYs
Population Value of Current Implement’n (VCI) QALYs
Maximum Population Value of Quality 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.
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