Appendix C

Summary of Empirical Modeling Methodology

The committee commissioned a body of empirical analyses to examine geographic variation in spending, utilization, and quality using public and commercial datasets. The goals of the analyses were to characterize and account for the presence and magnitude of geographic variation across different geographic units, payers, and clinical condition cohorts. The population-specific studies conducted by Acumen, LLC, The Lewin Group, and Harvard University were carried out using the research framework outlined in Table C-1.1 Precision Health Economics’ methodological approach in synthesizing these results and evaluating geographic variation in total health spending is then summarized. The complete methodological details are available in the subcontractor reports.2

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1This table only presents the methodology for the Medicare 2007-2009 analysis and does not show the Medicaid 2007-2009 analysis, the Medicare 1992-2010 growth analysis, or the Medicare Advantage 2007-2009 analysis, all of which use variations on this methodological approach.

2In addition to the studies summarized in Appendix C, the committee also commissioned reports from the University of Pittsburgh and the Dartmouth Institute for Health Policy and Clinical Practice. All papers can be accessed through the Institute of Medicine website via the following link: http://www.iom.edu/geovariationmaterials.



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Appendix C Summary of Empirical Modeling Methodology T he committee commissioned a body of empirical analyses to exam- ine geographic variation in spending, utilization, and quality using public and commercial datasets. The goals of the analyses were to characterize and account for the presence and magnitude of geographic variation across different geographic units, payers, and clinical condition cohorts. The population-specific studies conducted by Acumen, LLC, The Lewin Group, and Harvard University were carried out using the research framework outlined in Table C-1.1 Precision Health Economics’ method- ological approach in synthesizing these results and evaluating geographic variation in total health spending is then summarized. The complete meth- odological details are available in the subcontractor reports.2 1  This table only presents the methodology for the Medicare 2007-2009 analysis and does not show the Medicaid 2007-2009 analysis, the Medicare 1992-2010 growth analysis, or the Medicare Advantage 2007-2009 analysis, all of which use variations on this methodological approach. 2 In addition to the studies summarized in Appendix C, the committee also commissioned reports from the University of Pittsburgh and the Dartmouth Institute for Health Policy and Clinical Practice. All papers can be accessed through the Institute of Medicine website via the following link: http://www.iom.edu/geovariationmaterials. 131

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TABLE C-1 Acumen, Lewin, and Harvard’s Study Approach and Methodology in the Medicare and Commercial Analyses 132 Acumen Lewin Harvard Data Source Medicare Analysis: (Parts A, B, and Optum De-identified Normative Thomson Reuters MarketScan D) Claims and Enrollment data Health Information (dNHI) Database Commercial Claims and Encounters *Medicare Advantage (MA) (Part C) database was analyzed separately Years of Analysis 2007–2009 2007–2009 2007–2009 Study Population • 100% sample of all Medicare • Included enrollees between the • Included enrollees between the fee-for-service beneficiaries ages 0–64, with a small sample ages 0–64. • Majority of sample are over age over age 65. • The clinical condition cohort 65. The clinical condition cohort • The clinical condition cohort analyses were limited to ages analyses were limited to ages analyses were limited to ages 18–64. 18 and older. 18–64. • Excludes costs for beneficiaries in the months that they are enrolled in Medicare Advantage (Part C). Excludes all beneficiaries who have any third-party payment in the observation window. Treatment of • Not applicable • Excluded observations • Imputed value (6% of population) “Capitated Claims” (0.3% of population) Measurement • Total all-cause spending includes • Total all-cause spending includes • Total all-cause spending includes of Spending all costs incurred by Medicare and all costs of all facility, provider all costs of all medical and the patient in covering inpatient, and prescription drug costs prescription drug costs incurred outpatient, hospice, home health, incurred by payer, secondary by payer, secondary payer, and skilled nursing, carrier, durable payer and patient. patient. medical equipment and Part D • See Harvard input-price • See Harvard input-price claim types. adjustment memo (Appendix E) adjustment memo (Appendix E) • Medicare analysis follows input- price standardization methodol- ogy developed with CMS as

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part of the Hospital Value Based Purchasing (HVBP) program. • The inpatient claims exclude indi- rect medical education (IME) and disproportionate share (DSH). Measurement Measured in two ways: Measured in two ways: Measured in two ways: of Utilization • Counts per service • Counts per service • Counts per service • Input price-standardized cost • Output-price standardized cost • Output-price standardized cost (separate input and output (Harvard output-price adjustment (Harvard output-price adjustment price adjustment is unnecessary memo, Appendix E.3) memo, Appendix E.3) in this analysis, as Medicare sets final prices accounting for regional variation). Measurement • Follows the Agency for Healthcare • Aggregate analyses included • Created original composite of Quality Research and Quality methodol- AHRQ patient safety indicator measures: 4 domains of PQI, PSI, ogy for analyzing quality (PSI) #90, pediatric quality process measures, and readmis- • The aggregate quality composites indicator (PDI) #19, inpatient qual- sions within 30 days of discharge. include 8 PSI, 6 IQI, and 12 PQI ity indicator (IQI) #91 and preven- • Excluded analysis of PDI as these measures. tion quality indicator (PQI) #90. were rare in the dataset. • Separate quality measures • Cohort quality analyses were computed for 13 of the clinical limited those with adequate condition cohorts. sample sizes, namely coronary heart disease, diabetes, and low back pain. Multiple Regression Analyses Dependent Variables • OLS Regression without area • OLS Regression with area fixed • OLS Regression without area of Spending and fixed effects; estimated area level effects: fixed effects; estimated area level Utilization effects as average of residuals effects as average of residuals from first model estimation from first model estimation (Sen- • Did not “shrink” estimates sitivity analysis showed 0.98 corre- lation to fixed effects model) • Empirical Bayes framework of “shrinking” estimates used to cor- 133 rect for small sample size variation continued

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TABLE C-1 Continued 134 Acumen Lewin Harvard Market Level • Used a 2-stage regression method: • Used Harvard-developed market • Used a 2-stage regression method: Analysis Step 1: OLS regression without level measures (Appendix F.1). Step 1: OLS regression without fixed effects. • Used a 2-stage regression method: fixed effects. Step 2: First stage estimates of Step 1: OLS regression without Step 2: First stage estimates of area effects regressed against fixed effects. area effects regressed against a set of market-level measures Step 2: First stage estimates of a set of market-level measures (outlined in Appendix D). area effects regressed against (outlined in Appendix D). • Method comparable to Harvard; a set of market-level measures • Method comparable to Acumen; see Appendix F: Harvard Market (outlined in Appendix D). see Appendix F: Harvard Market Level Analysis Methodology • Used different set of market Level Analysis Methodology Memorandum (11.21.12). predictors. Memorandum (11.21.12). Quality Analyses • Conducted logistic regression • Conducted logistic regressions • Used logistic models for “rare” for the IQI and PSI, and OLS for the PSI and PQIs, predicted quality outcomes, and linear regression for the PQI composites. the outcomes at an individual models for all other outcomes. level and then averaged at area • The rate is risk adjusted by level to produce a rate. multiplying the ratio of observed • Risk adjusted based on covariates to expected outcomes by a from cluster regressions rather reference rate. than covariates used in the AHRQ methodology (PHE, p. 12). Model Specification Refer to Appendix D for the complete list of independent variables used by each subcontractor. Clusters Correlation Analyses “Within” Analysis • Examines the distribution • OLS regression specifications • Examines the distribution of of the ratios (10th percentile, followed. the ratios (10th percentile, 90th 90th percentile, min, max, etc.) • Wald Test on HSA fixed effects percentile, min, max, etc.) of of the highest-spending to within HRR. Statistical significance the highest-spending to lowest- lowest-spending HSAs by HRR indicated intra-regional variation spending HSAs within by HRR

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• Also performs an OLS regression in spending at HSA level not cap- • Also Performs an OLS regression tured by HRR dummy variables of HSA average spending on HRR • Intra-regional variation examined indicator variables, weighted by using CV for HSA PMPM spending. beneficiary months in each HSA. “Between” Analysis Reported Pearson correlation of: Reported Pearson and Spearman Examines correlations of: • Medicare beneficiary utilization correlations of: • HRR spending and rankings across across clinical condition cohorts • HRR spending and rankings across clinical condition cohorts • Medicare beneficiary utilization clinical condition cohorts • Correlation of spending and and quality, across condition • HRR quality and rankings across quality measures cohorts and in aggregate clinical condition cohorts • Correlation of quality across population • Correlation of spending and qual- cohorts ity measures 135

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136 VARIATION IN HEALTH CARE SPENDING Precision Health Economics Study Approach and Methodology Summary: The Precision Health Economics (PHE) report first synthesized and summarized the results from spending, utilization, and quality regression analyses of the population-specific studies conducted by Acumen, Lewin, and Harvard, allowing for easy comparison of findings across public and private payers. In order to examine variation “within” HRRs, PHE con- ducted a random effects regression of spending at utilization at the HSA level, with the random effects at the HRR level. Additionally, PHE created a measure of total health care spending, attempting to account for the total United States population by including spending for Medicare, Medicaid, commercially insured, and uninsured populations. This measure was created using the following steps: 1. Obtained spending estimates for Medicare, Medicare Advantage (or Medicare managed care), Medicaid, and commercially insured populations from the empirical analyses conducted by Acumen, Lewin, and Harvard. 2. Estimated spending for the uninsured and Medicaid managed care by HRR. 3. Created payer-specific weights to estimate unadjusted, total health care spending. The OptumInsight and MarketScan spending data were alternately used as “proxies” for commercial spending. 4. Created two measures of total PMPM spending by HRR, first un- adjusted and then adjusted for input prices. Both estimates were adjusted for age, sex, and health status. PHE conducted OLS regression analysis of total health care spending following methods used by other subcontractors in the individual studies. · Note, for reasons of parsimony, PHE created an index of “health status” rather than using the complete set of HCCs used in the Acumen studies of Medicare and Medicaid. · The market level analysis was also conducted using a reduced set of market covariates, selected according to several criteria: policy relevance, lack of redundancy, effect size in the population-specific studies, and, finally, the availability of consistent measurement of the predictors across payers. · Regressions were also weighted by the population in HRRs. The health status predictors were additionally weighted by that popula- tion’s share of the total HRR population.