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The American Journal of Managed Care August 2017
Health Insurance and Racial Disparities in Pulmonary Hypertension Outcomes
Kishan S. Parikh, MD; Kathryn A. Stackhouse, MD; Stephen A. Hart, MD; Thomas M. Bashore, MD; and Richard A. Krasuski, MD
Evaluation of a Hospital-in-Home Program Implemented Among Veterans
Shubing Cai, PhD; Patricia A. Laurel, MD; Rajesh Makineni, MS; Mary Lou Marks, RN; Bruce Kinosian, MD; Ciaran S. Phibbs, PhD; and Orna Intrator, PhD
The Effect of Implementing a Care Coordination Program on Team Dynamics and the Patient Experience
Paul Di Capua, MD, MBA, MSHPM; Robin Clarke, MD, MSHS; Chi-Hong Tseng, PhD; Holly Wilhalme, MS; Renee Sednew, MPH; Kathryn M. McDonald, MM, PhD; Samuel A. Skootsky, MD; and Neil Wenger, MD, MPH
What Do Pharmaceuticals Really Cost in the Long Run?
Darius Lakdawalla, PhD; Joanna P. MacEwan, PhD; Robert Dubois, MD, PhD; Kimberly Westrich, MA; Mikel Berdud, PhD; and Adrian Towse, MA, MPhil
The Hospital Tech Laboratory: Quality Innovation in a New Era of Value-Conscious Care
Courtland K. Keteyian, MD, MBA, MPH; Brahmajee K. Nallamothu, MD, MPH; and Andrew M. Ryan, PhD
Association Between Length of Stay and Readmission for COPD
Seppo T. Rinne, MD, PhD; Meredith C. Graves, PhD; Lori A. Bastian, MD; Peter K. Lindenauer, MD; Edwin S. Wong, PhD; Paul L. Hebert, PhD; and Chuan-Fen Liu, PhD
Risk Stratification for Return Emergency Department Visits Among High-Risk Patients
Katherine E.M. Miller, MSPH; Wei Duan-Porter, MD, PhD; Karen M. Stechuchak, MS; Elizabeth Mahanna, MPH; Cynthia J. Coffman, PhD; Morris Weinberger, PhD; Courtney Harold Van Houtven, PhD; Eugene Z. Oddone, MD, MHSc; Katina Morris, MS; Kenneth E. Schmader, MD; Cristina C. Hendrix, DNS, GNP-BC; Chad Kessler, MD; and Susan Nicole Hastings, MD, MHSc
Cost-Effectiveness Analysis of Vagal Nerve Blocking for Morbid Obesity
Jeffrey C. Yu, AB; Bruce Wolfe, MD; Robert I. Griffiths, ScD, MS; Raul Rosenthal, MD; Daniel Cohen, MA; and Iris Lin, PhD
Impact of Formulary Restrictions on Medication Use and Costs
Xian Shen, PhD; Bruce C. Stuart, PhD; Christopher A. Powers, PharmD; Sarah E. Tom, PhD, MPH; Laurence S. Magder, PhD; and Eleanor M. Perfetto, PhD, MS
Currently Reading
Geographic Variation in Medicare and the Military Healthcare System
Taiwo Adesoye, MD, MPH; Linda G. Kimsey, PhD, MSc; Stuart R. Lipsitz, SCD; Louis L. Nguyen, MD, MBA, MPH; Philip Goodney, MD; Samuel Olaiya, PhD; and Joel S. Weissman, PhD

Geographic Variation in Medicare and the Military Healthcare System

Taiwo Adesoye, MD, MPH; Linda G. Kimsey, PhD, MSc; Stuart R. Lipsitz, SCD; Louis L. Nguyen, MD, MBA, MPH; Philip Goodney, MD; Samuel Olaiya, PhD; and Joel S. Weissman, PhD
Geographic variation in healthcare spending and utilization within the Military Health System is higher and significantly correlated with Medicare across hospital referral regions.

Objectives: To compare geographic variation in healthcare spending and utilization between the Military Health System (MHS) and Medicare across hospital referral regions (HRRs).

Study Design: Retrospective analysis. 

Methods: Data on age-, sex-, and race-adjusted Medicare per capita expenditure and utilization measures by HRR were obtained from the Dartmouth Atlas for 2007 to 2010. Similarly, adjusted data from 2007 and 2010 were obtained from the MHS Data Repository and patients assigned to HRRs. We compared high- and low-spending regions, and computed coefficient of variation (CoV) and correlation coefficients for healthcare spending, hospital inpatient days, hip surgery, and back surgery between MHS and Medicare patients.

Results: We found significant variation in spending and utilization across HRRs in both the MHS and Medicare. CoV for spending was higher in the MHS compared with Medicare, (0.24 vs 0.15, respectively) and CoV for inpatient days was 0.36 in the MHS versus 0.19 in Medicare. The CoV for back surgery was also greater in the MHS compared with Medicare (0.47 vs 0.29, respectively). Per capita Medicare spending per HRR was significantly correlated to adjusted MHS spending (r = 0.3; P <.0001). Correlation in inpatient days (r = 0.29; P <.0001) and back surgery (r = 0.52; P <.0001) was also significant. Higher spending markets in both systems were not comparable; lower spending markets were located mostly in the Midwest.

Conclusions: In comparing 2 systems with similar pricing schemes, differences in spending likely reflect variation in utilization and the influence of local provider culture.

Am J Manag Care. 2017;23(8):e259-e264
  • Geographic variation in healthcare spending and utilization within the Military Health System (MHS) is higher and significantly correlated with Medicare across hospital referral regions. 
  • Results contrast the negative correlation in spending between Medicare and the commercial sector. 
  • Higher variation in the MHS may reflect inherent variability in the health status of the MHS population. 
  • Positive correlation between spending and certain measures of utilization may suggest a strong impact of local provider culture on utilization. 
  • Our findings inform research that explores the impact of pricing schemes and local provider culture on unwarranted variation in utilization and spending.
Geographic variation in per capita healthcare spending is a well-documented phenomenon; however, its causes are less defined.1-5 Many have argued that such variation is unwarranted, especially in light of research showing that spending may be negatively correlated with quality of care and does not result in improved health outcomes or patient satisfaction.3,6 

A number of possible explanations for geographic variation have been studied. In a price-adjusted analysis of geographic variation in spending, Gottlieb et al found that administered prices set by Medicare in different regions accounted for only a small fraction of the variation observed. They attributed the high variation in spending to differences in utilization across regions.7 

Differences in the underlying health status of populations may be another justifiable cause of variation. Although some researchers did not find that higher-spending regions cared for sicker patients,2,8,9 others suggest that regions with sicker patients exhibit higher spending.10,11 Such approaches are limited; however, higher-spending regions may be more likely to perform tests that diagnose illnesses that would otherwise not have been diagnosed.12 Others believe that local culture is the predominant source of regional variation, perhaps due to a “specialist-oriented” practice pattern in high-spending regions,2,3 or hypothesize that regional differences result from increased utilization related to supply of physicians and hospital resources. For instance, physicians have been shown to modify admission and discharge of their patients based on the availability of intensive care unit beds by admitting patients with less-severe illnesses or extending length of stay when more beds are available.13

Although the majority of geographic variation research has relied on Medicare data, 64.2% of the population is covered by private insurance compared with only 15.6% covered by Medicare.14 An avenue to better understanding the factors influencing geographic variation in healthcare spending is to compare these findings with other payers or providers. Such elucidation is important in defining policy and allocating national financial resources. If similar variation occurs in other health systems and in the same areas, it would suggest that a strong local provider culture may play a role in driving health spending, regardless of the payer. A few studies have examined variation in commercial healthcare systems to identify patterns and drivers of spending variation.15-17 

Chernew et al explored spending patterns by comparing Medicare spending with large commercial firm healthcare spending by hospital referral region (HRR).15 They found a higher degree of regional variation within the commercial insurance market compared with Medicare. Their results showed a negative correlation in per capita spending between payers both before and after adjusting for age and sex differences; however, their measure of utilization, inpatient days per capita, was significantly correlated due to the potential effect of local provider culture in both systems. In the US Department of Veterans Affairs (VA) healthcare system, the Congressional Budget Office initially found that geographic variation in per capita spending was lower compared with Medicare. However, while the variation in spending within the VA increased from 2001 to 2007, it fell in the Medicare program from 2001 to 2005.18 It appeared that the VA’s centrally funded system and systemwide evidence-based practice guidelines may not have been sufficient to minimize variation, which, again, was perhaps influenced by local practice patterns.

Another federal system worthy of analysis is the Military Health System (MHS). TRICARE, the healthcare benefit program for MHS beneficiaries, serves approximately 9.5 million active duty and retired military personnel and their dependents.19 Approximately 15% of beneficiaries are active duty service members; the remaining beneficiaries are predominantly spouses, children, and retirees.19 TRICARE is separate from the VA, which primarily caters to veterans; its beneficiaries are surprisingly comparable to the privately insured population in terms of sociodemographics. 

Our analysis focused on TRICARE Prime (a health maintenance organization [HMO]–like system) enrollees who were assigned a primary care manager to oversee their total care, making it more likely that complete episodes of care occurred within the purview of TRICARE. In addition, these beneficiaries had either very low or no out-of-pocket costs. Compared with the VA, whose beneficiaries receive care through a network of VA facilities, these enrollees can receive care in both the budget-based direct care system (in Military Treatment Facilities [MTFs]) and the fee-for-service (FFS) purchased care system (managed by civilian providers).18 Because Prime enrollees tend to be concentrated near military facilities, the percentage of direct care provision varies significantly. TRICARE therefore offers a unique perspective on variation given its centrally managed dual system of care and its use of administrative pricing in the community setting. Taken together, these characteristics suggest lower geographic variation in costs and utilization than for other payers, although local market factors and practice styles may still play a role. 

We had 2 analytic goals: 1) to investigate variation by HRR in the MHS and 2) to compare the variation in the MHS with Medicare as documented in the Dartmouth Atlas, which compiles data from the American Medical Association’s master file and the American Hospital Association’s annual survey.20 We analyzed per capita costs and 3 utilization measures—hospital inpatient days, back surgery, and hip replacement surgery—to explore the possible effects of discretion on variation. We hypothesized that, given the centralized system of care within the MHS, geographic variation in healthcare spending and utilization within the MHS would be lower compared with Medicare.


Data Sources

Data were obtained from 2 sources: Medicare age-, sex-, and race-adjusted Part A and Part B per capita spending and utilization for 306 HRRs were obtained from the Dartmouth Atlas for the years 2007 to 2010.20 These estimates represented 20% of the FFS population and relied on Medicare claim files.20 We included Medicare beneficiaries 65 years or older and total spending (including fees for physicians, hospital and skilled nursing facilities, outpatient facilities, home health agencies, hospice care, and durable medical equipment). Spending rates, as documented in the Dartmouth Atlas, were calculated from Medicare claims; patients enrolled in HMOs were excluded.

The total MHS per capita spending and utilization data for 2007 and 2010 were obtained from the MHS Data Repository. Spending data for purchased care, which used administered pricing similar to Medicare, were obtained from claims using variables representing total payments made. Direct care costs were patient-level cost allocations of the total costs of an MTF, based on clinic-reported workload and expenses. Total costs from the direct care sector and payments from the purchased care sector thus represent the overall cost to TRICARE. In 2016,approximately 4.9 million beneficiaries were enrolled in TRICARE prime, and of those, 32% (1.58 million) were on active duty.19 Beneficiaries included active duty military personnel, retirees, and dependents older than 18 years residing within the United States and enrolled in a TRICARE Prime region within the United States. Overseas enrollees, wards, foster children, step children, step parents, and former spouses were excluded from the analysis, in addition to beneficiaries with missing or inconsistent sponsor information (2.8% to 3% of enrollees), resulting in a study population of approximately 3.2 million. This study was approved by the Partners Institutional Review Board (Protocol #2011P002080).


Patients in the MHS were assigned to 1 of 306 HRRs based on residence zip codes, using the zip code crosswalk obtained from the Dartmouth Atlas.20 Adjustments for age, race, and gender within the MHS were performed using the indirect method according to the Dartmouth Atlas methodology.20 In this process, missing race was imputed for dependents using the sponsor’s race (40.3%), following the methods of Stewart et al.21 

Mean per capita spending and utilization rates were determined across all 4 years for each HRR in both healthcare systems. Coefficients of variation (CoVs) and interquartile ranges (IQRs) were calculated for spending and utilization measures. We calculated the correlation for MHS versus Medicare spending and utilization and examined high- and low-spending HRRs for each. The threshold for statistical significance was P <.05. We also examined market characteristics, including physician and hospital resources of the high- and low-spending MHS and Medicare HRRs for the year 2006. These were also obtained from the Dartmouth Atlas. 


The average number of enrollees per year was 3.2 and 5.1 million within the MHS and Medicare groups, respectively. The populations differed demographically. Approximately 34% of the Medicare cohort was 80 years or older, the average proportion of females was 57.6%, and the highest proportion of black beneficiaries was 6.3%. Within our MHS Prime population, 4.4% of beneficiaries were 65 years or older. Females represented 45% of beneficiaries, of whom approximately 17% were African American. Medicare patients were more geographically dispersed within the United States, with a few highly populated HRRs located on the East Coast. The MHS beneficiaries were mostly located in the Midwest and southern United States (Figure). Per capita Medicare spending per HRR from 2007 to 2010 averaged $8752 (range = $6300-$15,000). As expected, this was more than 3 times higher than the MHS, with an average per capita spending of $2504 (range = $1223-$5221). About utilization, Medicare beneficiaries averaged 1.62 hospital inpatient days per year, which is greater in frequency than that of MHS beneficiaries, at 0.49. In addition, Medicare patients underwent more hip and back surgeries per 1000 patients than MHS beneficiaries, as would be expected in an older population (Table 1).

There was also significant variation in spending and utilization across HRRs in both the MHS and Medicare populations. The CoV for spending was higher in the MHS compared with Medicare (0.24 vs 0.15, respectively), and the IQR (75%/25%) was also higher in the MHS (1.41 vs 1.21, respectively) (Table 2). 

Similarly, measures of utilization showed greater variation within the MHS: the CoV for inpatient days was 0.36 in the MHS compared with 0.19 in Medicare; the CoV for back surgery was 0.47 versus 0.29, respectively; and the CoV for hip surgery was 0.69 versus 0.23.

Adjusted per capita Medicare spending per HRR was moderately, but significantly, correlated with adjusted MHS spending (r = 0.3; P <.0001). Similarly, correlation in hospital inpatient days between both systems was significant (r = 0.29; P <.0001). Back surgery was highly correlated between both systems (r = 0.52; P <.0001); however, hip surgery was not significantly related. 

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