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The American Journal of Managed Care August 2014
Personalized Preventive Care Reduces Healthcare Expenditures Among Medicare Advantage Beneficiaries
Shirley Musich, PhD; Andrea Klemes, DO, FACE; Michael A. Kubica, MBA, MS; Sara Wang, PhD; and Kevin Hawkins, PhD
Impact of Hypertension on Healthcare Costs Among Children
Todd P. Gilmer, PhD; Patrick J. O'Connor, MD, MPH; Alan R. Sinaiko, MD; Elyse O. Kharbanda, MD, MPH; David J. Magid, MD, MPH; Nancy E. Sherwood, PhD; Kenneth F. Adams, PhD; Emily D. Parker, MD, PhD; and Karen L. Margolis, MD, MPH
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Tracking Spending Among Commercially Insured Beneficiaries Using a Distributed Data Model
Carrie H. Colla, PhD; William L. Schpero, MPH; Daniel J. Gottlieb, MS; Asha B. McClurg, BA; Peter G. Albert, MS; Nancy Baum, PhD; Karl Finison, MA; Luisa Franzini, PhD; Gary Kitching, BS; Sue Knudson, MA; Rohan Parikh, MS; Rebecca Symes, BS; and Elliott S. Fisher, MD
Massachusetts Health Reform and Veterans Affairs Health System Enrollment
Edwin S. Wong, PhD; Matthew L. Maciejewski, PhD; Paul L. Hebert, PhD; Christopher L. Bryson, MD, MS; and Chuan-Fen Liu, PhD, MPH
Contemporary Use of Dual Antiplatelet Therapy for Preventing Cardiovascular Events
Andrew M. Goldsweig, MD; Kimberly J. Reid, MS; Kensey Gosch, MS; Fengming Tang, MS; Margaret C. Fang, MD, MPH; Thomas M. Maddox, MD, MSc; Paul S. Chan, MD, MSc; David J. Cohen, MD, MSc; and Jersey Chen, MD, MPH
Potential Benefits of Increased Access to Doula Support During Childbirth
Katy B. Kozhimannil, PhD, MPA; Laura B. Attanasio, BA; Judy Jou, MPH; Lauren K. Joarnt; Pamela J. Johnson, PhD; and Dwenda K. Gjerdingen, MD
Synchronization of Coverage, Benefits, and Payment to Drive Innovation
Annemarie V. Wouters, PhD; and Nancy McGee, JD, DrPH
The Effect of Depression Treatment on Work Productivity
Arne Beck, PhD; A. Lauren Crain, PhD; Leif I. Solberg, MD; Jürgen Unützer, MD, MPH; Michael V. Maciosek, PhD; Robin R. Whitebird, PhD, MSW; and Rebecca C. Rossom, MD, MSCR
Economic Implications of Weight Change in Patients With Type 2 Diabetes Mellitus
Kelly Bell, MSPhr; Shreekant Parasuraman, PhD; Manan Shah, PhD; Aditya Raju, MS; John Graham, PharmD; Lois Lamerato, PhD; and Anna D'Souza, PhD
Optimizing Enrollment in Employer Health Programs: A Comparison of Enrollment Strategies in the Diabetes Health Plan
Lindsay B. Kimbro, MPP; Jinnan Li, MPH; Norman Turk, MS; Susan L. Ettner, PhD; Tannaz Moin, MD, MBA, MSHS; Carol M. Mangione, MD; and O. Kenrik Duru, MD, MSHS
Does CAC Testing Alter Downstream Treatment Patterns for Cardiovascular Disease?
Winnie Chia-hsuan Chi, MS; Gosia Sylwestrzak, MA; John Barron, PharmD; Barsam Kasravi, MD, MPH; Thomas Power, MD; and Rita Redberg MD, MSc
Effects of Multidisciplinary Team Care on Utilization of Emergency Care for Patients With Lung Cancer
Shun-Mu Wang, MHA; Pei-Tseng Kung, ScD; Yueh-Hsin Wang, MHA; Kuang-Hua Huang, PhD; and Wen-Chen Tsai, DrPH
Health Economic Analysis of Breast Cancer Index in Patients With ER+, LN- Breast Cancer
Gary Gustavsen, MS; Brock Schroeder, PhD; Patrick Kennedy, BE; Kristin Ciriello Pothier, MS; Mark G. Erlander, PhD; Catherine A. Schnabel, PhD; and Haythem Ali, MD

Tracking Spending Among Commercially Insured Beneficiaries Using a Distributed Data Model

Carrie H. Colla, PhD; William L. Schpero, MPH; Daniel J. Gottlieb, MS; Asha B. McClurg, BA; Peter G. Albert, MS; Nancy Baum, PhD; Karl Finison, MA; Luisa Franzini, PhD; Gary Kitching, BS; Sue Knudson, MA; Rohan Parikh, MS; Rebecca Symes, BS; and Elliott S. Fisher, MD
The authors demonstrate the utility of distributed data models for reporting of local trends and variation in utilization, pricing, and spending for commercially insured beneficiaries.
To explore the feasibility of using a distributed data model for ongoing reporting of local healthcare spending, specifically to investigate the contribution of utilization and pricing to geographic variation and trends in reimbursements for commercially insured beneficiaries younger than 65 years.

Study Design
Retrospective descriptive analysis.

Commercial claims were obtained for beneficiaries in 5 states for the years 2008 to 2010 using a distributed data model. Claims were aggregated to the hospital service area (HSA) level and healthcare utilization was quantified using a novel, National Quality Forum–endorsed measure that is independent of price and allows for the calculation of resource use across all services in standardized units. We examined trends in utilization, prices, and reimbursements over time. To examine geographic variation, we mapped resource use by HSA in the 3 states from which we had data from multiple insurers. We calculated the correlation between commercial and Medicare reimbursements and utilization. Medicare claims were obtained from the Dartmouth Atlas.

We found that much of the recent growth in reimbursements for the commercially insured from 2008 to 2010 was due to increases in prices, particularly for outpatient services. As in the Medicare population, resource use by this population varied by HSA. While overall resource use patterns in the commercially insured did not mirror those among Medicare beneficiaries, we observed a strong correlation in inpatient hospital use.

This research demonstrates the feasibility and value of public reporting of standardized area-level utilization and price data using a distributed data model to understand variation and trends in reimbursements.

Am J Manag Care. 2014;20(8):650-657
This study explored the use of a distributed data model and a novel method for calculating utilization to track local trends in spending for commercially insured beneficiaries.
  • Recent growth in spending for commercially insured beneficiaries was due principally to increases in prices, rather than increases in utilization.
  • Commercial utilization and spending varied across local areas and was not highly correlated with Medicare utilization and spending.
  • A distributed model may allow for nationwide reporting of spending and utilization to track the local effects of healthcare reform efforts.
Timely, local data are important to policy-makers, providers, patients, payers, and employers working to slow the growth of healthcare spending, which is a major focus of federal, state, and local healthcare reform initiatives. Community-based multistakeholder coalitions have formed across the country in an effort to influence their local healthcare markets and reduce costs. More than 40 percent of people in the United States live in a community with a multistakeholder coalition aimed at improving health and healthcare, including collaboratives focused on improving the exchange of health information, accelerating engagement by key local opinion leaders and stakeholders, or promoting quality improvement.1 All of these entities, however, lack the local data needed to determine if their efforts are making a difference.        

The factors contributing to rising healthcare spending differ across communities and depend on local context; understanding the drivers of local spending growth is complicated by the variety of inputs. Provider culture and supply, various market segments (outpatient, inpatient, long-term care), payer mix, regulation, and the competitiveness of hospital and physician markets all affect pricing, utilization, and ultimately, the total cost of care.

Research has shown that the relative contribution of these factors varies across markets and that drivers of commercial spending are not necessarily the same as drivers of Medicare spending. Chernew et al found that commercial spending was not correlated with Medicare spending across hospital referral regions.2 Examining the commercial and Medicare populations for El Paso and McAllen, Texas, Franzini et al found there was 86% greater per capita Medicare spending in McAllen than El Paso, but 7 percent less commercial spending.3 A recent Institute of Medicine report found that regional variation in spending in the commercial insurance market is due in large part to differences in price markups by providers. Differences in utilization, however, still explained over 30% of the regional variation in spending.4

These diverse results indicate that the factors that contribute to rising healthcare spending are multifactorial and highlight the need for more comprehensive and detailed analysis of commercial data. The 2013 report issued by the Institute of Medicine, Variation in Health Care Spending: Target Decision Making, Not Geography, was unprecedented in that it combined both Medicare and commercial data from a variety of proprietary sources for analysis. The report underscored the importance of available data on both commercial and Medicare utilization and prices at a local level. These data could be useful to engage local stakeholders (such as employers), identify areas of potential waste, point regulators to localities where monopolistic pricing may be occurring, and evaluate the impact of local and national reform initiatives.4

There is substantial uncertainty about the best approach to make local data publicly available. Feasible options might include a single federal database,5 combining state all-payer data (currently available in Colorado, Kansas, Maine, Maryland, Massachusetts, Minnesota, New Hampshire, Tennessee, Utah, and Vermont),6 using private data aggregators, or relying on a distributed data model.7 The Health Care Cost Institute has combined data from 4 large commercial insurers and has published broad reports on trends in employer-sponsored insurance, but has not produced any data at a local level.8,9 There are some community-level efforts under way to track healthcare spending and utilization locally, most notably in California, but they are relatively rare.10 In spite of the Institute of Medicine’s call for making more and better data available on both Medicare and commercial populations, it is not clear how this might be done.

In this paper we explore the feasibility and value of 2 potential approaches for making commercial spending and utilization data available at a local level. First, we work with an all-payer data set for New Hampshire, Maine, and Vermont, aggregated by Onpoint Health Data, a private, nonprofit organization. Second, we use a distributed data model to aggregate data from a single payer in 2 states. Our methods include a standardized approach to measuring utilization, and our partners submitted utilization and spending reports that were stripped of protected health information. These reports allowed us to aggregate data at a hospital service area (HSA) level and adjust for demographic characteristics. The findings demonstrate the feasibility of each of these approaches and the resulting data highlight the potential utility of tracking local healthcare spending.


  To test the distributed data model, we used data from 3 distinct sources. We obtained data on utilization and reimbursements for commercial beneficiaries in Maine, New Hampshire, and Vermont from an all-payer data set (2008-2010), along with data on beneficiaries of Blue Cross Blue Shield of Michigan (2009-2010) and Texas (2008- 2010) plans. The latter 2 states were chosen because of existing research relationships with relevant data providers. Each of the participating analytic teams applied standard software to measure utilization and submitted reports detailing summary information for beneficiaries in each age and gender category for each HSA. HSAs were defined in previous work by identifying the zip codes where the highest proportion of Medicare beneficiaries received their care from a single hospital.11 Data were then aggregated by the study authors and compared with corresponding HSA data from the Medicare program.

Study Populations

Each participating organization applied a standardized approach for defining the commercial population to be included in the analysis. Beneficiaries were required to be enrolled for a minimum of 9 months during the year, unless the member was born during the calendar year or was older than 65 years by the end of the year.

Data Management

  Participating health plans and data providers used software developed by HealthPartners, a nonprofit healthcare organization and health plan, to measure a utilization amount associated with each insurance claim. The HealthPartners algorithm quantifies utilization using Total Care Relative Resource Values (TCRRVs), which are the basis for the National Quality Forum (NQF)-endorsed Total Resource Use measure.12 TCRRVs, which are expressed in dollars, measure the utilization and intensity of the services delivered to manage a patient’s healthcare needs. We chose to use the HealthPartners measure because it is independent of price and allows for the weighting and calculation of resource use across all medical services in standardized units. We did not want to obscure the effect of regional price differences on overall reimbursement amounts. In addition, we chose to use the measure because it makes resource use amounts equivalent for services offered across multiple settings. 

TCRRVs are unique in that they are relative within and across components of care (inpatient, outpatient, professional, and pharmacy), which allows for the isolation of resource use not only by component, but also on a total, per capita basis. The TCRRVs are made relative within the components of care using the CMS weighting system, including Medicare Severity Diagnosis Related Groups for inpatient care, Ambulatory Payment Classifications for outpatient care, and Relative Value Units for professional office care, while the TCRRVs for pharmaceutical expenditures are made relative by using the median average wholesale price per day for each National Drug Code. TCRRVs are calculated by multiplying the CMS weight by national average paid amount and the relevant service count. While the Health Care Cost Institute also uses CMS weights to measure the intensity of care, its methodology does not involve reporting (1) a resource use amount based on both utilization and intensity, or (2) a measure of total resource use across service lines.

The TCRRV algorithm was applied at the claim level by each data contributor and aggregated at the HSA level for transmission to study authors. (TCRRV weights are updated annually to correspond to updates in the CMS weight files. We used the 2011 TCRRV national weights.) Resource use was capped at $100,000 for each beneficiary in a given year and all components of care were reduced proportionally for both reimbursements and resource use. Very large resource use amounts greatly skew the mean in populations where the majority of beneficiaries have very low utilization and often represent extreme, unavoidable events. Capping reduced average resource use in 2008-2010 by an average of 11% across all HSAs (9.3% and 13.6% for HSAs in the lowest and highest quintile of resource use, respectively).

Both reimbursement and resource use (ie, utilization) amounts for each component of care (inpatient, outpatient, professional, and pharmacy) were transmitted to the study authors, as were denominator data (age in groups, gender, HSA of residence, number of people, and number of person-years). The HealthPartners software performs quality control checks at the claim level by comparing calculated resource use with the reimbursement amount. If the calculated resource use for a particular claim was outside of normal limits, we imputed resource use using the reimbursement amount multiplied by the ratio between resource use and reimbursement amount for all normal claims from that state, year, and component of care. The combination of numerator and denominator data allowed for adjustment and rate calculations. The transmitted data also included the prevalence of prescription drug and mental health carve-outs for each HSA. The Blue Cross Blue Shield of Michigan data did not include HSAspecific information on carve-out levels, but did include the overall state values by year. We used this information and exploratory models from the other states to impute prescription drug reimbursement and utilization values for Michigan HSAs. The imputation assumed a constant carve-out rate across the state each year.

Statistical Methods

Age, gender, and carve-out adjustment was performed using linear regression across the 5 states. Weighted averages for each HSA were calculated using the adjusted values for each year and component of care. State averages were created by weighting by the number of beneficiaries in each HSA. HSA average relative prices were defined as the ratio of resource use to the reimbursement amount. We normalized relative prices to 1 across all 5 states. Only HSAs with a sum of more than 1000 commercial beneficiaries over the study period were included. Analysis was also limited to those HSAs that were either in 1 of the 5 target states or overlapped the border of 1 of the 5 target states. These 2 exclusion factors reduced the total number of people per calendar year in the analyses by less than 0.1%. 

To examine geographic variation, we mapped resource use for each HSA in Maine, New Hampshire, and Vermont, reflecting procedures and services offered across all 4 components of care. We divided the HSAs into quintiles to display differences in resource use. Maps were not generated for Michigan or Texas, because data were from a single insurer in each state.

Medicare Program Comparison

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