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The American Journal of Managed Care March 2015
Evaluation of Care Management Intensity and Bariatric Surgical Weight Loss
Sarit Polsky, MD, MPH; William T. Donahoo, MD; Ella E. Lyons, MS; Kristine L. Funk, MS, RD; Thomas E. Elliott, MD; Rebecca Williams, DrPh, MPH; David Arterburn, MD, MPH; Jennifer D. Portz, PhD, MSW; and Elizabeth Bayliss, MD, MSPH
Potential Savings From Increasing Adherence to Inhaled Corticosteroid Therapy in Medicaid-Enrolled Children
George Rust, MD, MPH, FAAFP, FACPM; Shun Zhang, MD, MPH; Luceta McRoy, PhD; and Maria Pisu, PhD
Innovation in Plain Sight
Karen Ignagni, MBA, President and Chief Executive Officer, America's Health Insurance Plans
Early Changes in VA Medical Home Components and Utilization
Jean Yoon, PhD, MHS; Chuan-Fen Liu, PhD, MPH; Jeanie Lo, MPH; Gordon Schectman, MD; Richard Stark, MD; Lisa V. Rubenstein, MD, MSPH; and Elizabeth M. Yano, PhD, MSPH
Are Healthcare Quality "Report Cards" Reaching Consumers? Awareness in the Chronically Ill Population
Dennis P. Scanlon, PhD; Yunfeng Shi, PhD; Neeraj Bhandari, MD; and Jon B. Christianson, PhD
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Developing a Composite Weighted Quality Metric to Reflect the Total Benefit Conferred by a Health Plan
Glen B. Taksler, PhD; and R. Scott Braithwaite, MD, MSc, FACP
Cost Differential by Site of Service for Cancer Patients Receiving Chemotherapy
Jad Hayes, MS, ASA, MAAA; J. Russell Hoverman, MD, PhD; Matthew E. Brow, BA; Dana C. Dilbeck, BA; Diana K. Verrilli, MS; Jody Garey, PharmD; Janet L. Espirito, PharmD; Jorge Cardona, BS; and Roy Beveridge, MD
The Combined Effect of the Electronic Health Record and Hospitalist Care on Length of Stay
Jinhyung Lee, PhD; Yong-Fang Kuo, PhD; Yu-Li Lin, MS; and James S. Goodwin, MD
Strategy for a Transparent, Accessible, and Sustainable National Claims Database
Robin Gelburd, JD, BA
Treatment Patterns, Healthcare Utilization, and Costs of Chronic Opioid Treatment for Non-Cancer Pain in the United States
David M. Kern, MS; Siting Zhou, PhD; Soheil Chavoshi, MS; Ozgur Tunceli, PhD; Mark Sostek, MD; Joseph Singer, MD; and Robert J. LoCasale, PhD
Trends in Mortality Following Hip Fracture in Older Women
Joan C. Lo, MD; Sowmya Srinivasan, MD; Malini Chandra, MS, MBA; Mary Patton, MD; Amer Budayr, MD; Lucy H. Liu, MD; Gene Lau, MD; and Christopher D. Grimsrud, MD, PhD
Long-Term Outcomes of Analogue Insulin Compared With NPH for Patients With Type 2 Diabetes Mellitus
Julia C. Prentice, PhD; Paul R. Conlin, MD; Walid F. Gellad, MD, MPH; David Edelman, MD; Todd A. Lee, PharmD, PhD; and Steven D. Pizer, PhD
Factors Affecting Medication Adherence Trajectories for Patients With Heart Failure
Deborah Taira Juarez, ScD; Andrew E. Williams, PhD; Chuhe Chen, PhD; Yihe Goh Daida, MS; Sara K. Tanaka, MPH; Connie Mah Trinacty, PhD; and Thomas M. Vogt, MD, MPH

Developing a Composite Weighted Quality Metric to Reflect the Total Benefit Conferred by a Health Plan

Glen B. Taksler, PhD; and R. Scott Braithwaite, MD, MSc, FACP
The authors developed a weighted quality measure to reflect the total health benefit conferred by a health plan annually to its members.
To improve individual health quality measures, which are associated with varying degrees of health benefit, and composite quality metrics, which weight individual measures identically.

Study Design: We developed a health-weighted composite quality measure reflecting the total health benefit conferred by a health plan annually, using preventive care as a test case.

Methods: Using national disease prevalence, we simulated a hypothetical insurance panel of individuals aged 25 to 84 years. For each individual, we estimated the gain in life expectancy associated with 1 year of health system exposure to encourage adherence to major preventive care guidelines, controlling for patient characteristics (age, race, gender, comorbidity) and variation in individual adherence rates. This personalized gain in life expectancy was used to proxy for the amount of health benefit conferred by a health plan annually to its members, and formed weights in our health-weighted composite quality measure. We aggregated health benefits across the health insurance membership panel to analyze total health system performance.

Results: Our composite quality metric gave the highest weights to health plans that succeeded in implementing tobacco cessation and weight loss. One year of compliance with these goals was associated with 2 to 10 times as much health benefit as compliance with easier-to-follow preventive care services, such as mammography, aspirin, and antihypertensives. For example, for women aged 55 to 64 years, successful interventions to encourage weight loss were associated with 2.1 times the health benefit of blood pressure reduction and 3.9 times the health benefit of increasing adherence with screening mammography.

Conclusions: A single health-weighted quality metric may inform measurement of total health system performance.

Am J Manag Care. 2015;21(3):221-227
We developed a weighted quality measure to reflect the total health benefit conferred by a health plan annually to its members.  
  • Quality was weighted by the gain in life expectancy associated with 1 year of member adherence.
  • Health plans that helped members quit smoking or lose weight for 1 year contributed 2 to 10 times as much health benefit as those that improved member compliance with easier-to follow preventive care services, such as mammography, aspirin, and antihypertensives.
  • Weighted metrics may inform overall health plan evaluation—for example, by reporting the total insurance plan quality next to price on health insurance exchanges.
Measurement of health system performance is difficult. Quality measures are proliferating1,2 with >350 suggested metrics in 2014,3 and these measures are often criticized because they focus on narrowbased interventions (eg, use of angiotensin-converting enzyme inhibitors for patients with congestive heart failure) that fail to provide a global measure of benefit.4-6 Existing metrics also have varying correlations with health outcomes of interest to patients and patient representatives;4-8 therefore, it is unknown whether a health plan that exceeds quality measures confers greater health benefit than other health plans.9,10 Moreover, composite evaluation metrics such as the Healthcare Effectiveness and Data Information Set typically assign the same weight to each item. For example, the percentage of women aged 40 to 69 years who had a screening mammogram in the past 2 years counts as much as the percentage of diabetics aged 18 to 75 years with controlled A1C,11 even though their contributions to health may differ.

A more appropriate weighting scheme for composite metrics remains subject to debate. Some researchers have suggested all-or-none or 70% standards, in which patients must meet all or ≥70% of identified criteria, respectively, to be included in the numerator.12-14 Such criteria are used in the evaluation of accountable care organizations for diabetes and coronary artery disease, such as the percent of adults with ischemic vascular disease who received ≥1 lipid profile within 12 months and whose most recent low-density lipoprotein cholesterol (LDL-C) level was <100 mg/dL.1 Others have argued for development of composite weights based on measurement reliability,15 post surgical quality of life,16 or population health.12 No consensus on optimal quality weights has been reached.

More generally, the National Quality Forum requires 4 criteria for endorsement of a quality measure: 1) a focus on a priority area that improves healthcare quality, 2) scientific acceptability (reliability and validity), 3) usability and relevance, and 4) feasibility.17 To improve their usefulness, some researchers suggest that quality measures must transition to broader, more meaningful metrics, which readily lend themselves to improvement.4 Yet, the development of appropriate measures and their implementation remain at early stages; for example, while electronic medical records (EMRs) may enable better measurement of patient care and outcomes, they may under- or overestimate appropriate care utilization. A recent study in a federally qualified health center found that EMRs estimated 38% appropriate asthma medication use, compared with 77% when measured manually18,19; similar inaccuracies have been found using claims data.20

To consider the potential to condense multiple quality metrics into more actionable, useful information, we aimed to develop a health-weighted composite quality metric, which quantified the amount of preventive health benefit conferred by a health system over 1 year to a typical health insurance system membership panel. Such a measure may complement existing quality metrics by giving a more holistic view of the health benefit imparted by health systems, and may be useful for patients and patient representatives (employers) seeking to compare different health systems or to evaluate whether a health system is improving over time.


We adapted a previously published and validated computer simulation20 to estimate the increase in life expectancy that would occur from 1 year of exposure to a health system with specified performance on individual quality metrics on a population with specified distributions of characteristics. Briefly, that model estimated life expectancy for an individual patient, personalized for age, gender, race, risk factors, and comorbidity.21 The model then simulated the change in life expectancy associated with adherence to each US Preventive Services Task Force (USPSTF) grade A and B recommendation, and rank-ordered results to help identify the most important preventive care guidelines for each patient.21

We targeted the question: “How much additional health has a health plan conferred to a particular patient in the past year, through preventive care?” by making 3 main adaptions to this model. First, we teased apart the health benefit attributable to 1 year of health plan intervention (“attributability”), disambiguating it from additional benefit that might occur in successive years if continued intervention made adherence more likely. For example, we only considered the benefit from taking a lipid-lowering medication that arose from 1 year of health plan intervention, assuming that patient’s adherence would decline in succeeding years unless its persistence was reinforced by subsequent care.

Second, because adherence to medications and lifestyle recommendations are important contributors to the success of chronic disease management and prevention, we modeled both intervention-specific adherence rates (for example, assuming that it was easier to take antihypertensive medications than to stop smoking) and variability in adherence rates across health plan members (above- vs below-average member-specific adherence rates). In this context, “adherence” was defined as compliance with each USPSTF recommendation within 1 year of working with a physician: body mass index (BMI) = 24.9; blood pressure (BP) = 120/80 mm Hg; total cholesterol = 190 mg/dL; LDL-C = 70 mg/dL; no tobacco use; no alcohol misuse; for diabetics, A1C = 6% (controlled diabetes); and mammograms, Pap smears, and colonoscopies at recommended frequencies.21

Finally, in addition to a baseline lifetime horizon, we considered shorter (5-, 10-, and 20-year) time horizons that may be more relevant to health insurers and employers who expect their members to eventually change insurance plans. eAppendix A provides mathematical details (available at

Hypothetical Insured Membership Panel

We constructed a hypothetical health insurance system membership panel using national prevalence of age, race, gender, and comorbidity as a proxy. We focused on individuals aged 25 to 84 years (approximately 64% of the US population)22 as the core group for preventive healthcare exposure, using the most recent (July 2012) age-genderrace distribution reported by the US Census Bureau.22,23 We then used data from the National Health Interview Survey on the prevalence of select comorbidities, by age and gender, including obesity (estimated by the proportion of BMIs 25.0-29.9, 30.0-34.9, 35.0-39.9, ≥40.0), hypertension, hyperlipidemia, diabetes, tobacco use, and hazardous or harmful alcohol use (≥5 drinks/day on ≥12 occasions in the past year).24 Using the Surveillance, Epidemiology, and End Results Cancer Statistics Review, we also assumed a family history (≥1 first-degree relative) of colorectal cancer for 5% of individuals (of all ages/genders) and a family history of breast cancer for 12.5% of women.25

Additionally, we recognized that some individuals were more likely to suffer from multiple comorbidities. Therefore, we included prior estimates of the relative risks of diabetes and hypertension in obese (BMI ≥30.0) versus nonobese individuals (relative risk [RR] for diabetes, 5.2 for males and 12.7 for females; RR for hypertension, 2.6 for males and 4.2 for females).21 For example, among females aged 45 to 54 years, we estimated that 18.7% were obese hypertensive women, 18.1% were obese nonhypertensive women, 12.5% were nonobese hypertensive women, and 50.6% were nonobese nonhypertensive women (eAppendix A). Naturally, a more complete analysis would include representations of all permutations of comorbidities; however, data on correlations across all major comorbidities are not readily available, so we focused on those that were most prevalent and clinically important. We then simulated a basic medical history for each hypothetical health insurance member by sampling simple distributions for BMI, BP, cholesterol, and diabetes (eAppendix A).

Intervention-Specific Adherence Rates

For the sake of analytical tractability, we made a simplistic assumption, classifying each USPSTF recommendation as intrinsically “easy” or “difficult” to adhere to—a characteristic distinct from, but potentially interacting with, a patient’s propensity to adhere. Reported levels of adherence in published literature are heterogeneous and often dated,26,27 so we used prior studies in conjunction with our clinical judgment to arrive at these classifications. The set of “difficult” recommendations was composed of weight loss, tobacco cessation, reduction in alcohol use, healthy diet, and lifestyle (nonmedication) components of cholesterol and hypertension management. For difficult recommendations, we assumed adherence rates of 30% during the first year, based on reported adherence rates to tobacco cessation (which ranged from 20% to 40% after 26 weeks).26 All other recommendations were considered “easy,” for which we assumed adherence rates of 70% during the first year. These adherence rates were primarily based on compliance rates with antihypertensive26,27 and statin28,29 medications, though prior literature had a wide range of estimates. The first-year rate of adherence was followed by a lower probability of continued adherence after 1 year, reflecting what might happen if the health system did not effectively reinforce adherence in subsequent years. (eAppendix A provides further details.)


We developed a composite quality metric with weights that reflected the relative quantities of health benefit conferred across a typical health insurance system membership panel. Table 1 shows summary statistics: across all ages, the 3 most prevalent conditions were overweight or obesity, hypertension, and tobacco use.

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