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

 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.

 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; 

 <.0001). Correlation in inpatient days (r = 0.29; 

 <.0001) was also significant. Higher spending markets in both systems were not comparable; lower spending markets were located mostly in the Midwest.

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

		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.

 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.

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.

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,

 others suggest that regions with sicker patients exhibit higher spending.

 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.

 Others believe that local culture is the predominant source of regional variation, perhaps due to a “specialist-oriented” practice pattern in high-spending regions,

 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.

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.

 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.

Chernew et al explored spending patterns by comparing Medicare spending with large commercial firm healthcare spending by hospital referral region (HRR).

 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.

 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.

 Approximately 15% of beneficiaries are active duty service members; the remaining beneficiaries are predominantly spouses, children, and retirees.

 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).

 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.

 We analyzed per capita costs and 3 utilization measures—hospital inpatient days, back surgery, and hip replacement surgery&mdash;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 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.

 These estimates represented 20% of the FFS population and relied on Medicare claim files.

 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.

 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.

 Adjustments for age, race, and gender within the MHS were performed using the indirect method according to the Dartmouth Atlas methodology.

 In this process, missing race was imputed for dependents using the sponsor’s race (40.3%), following the methods of Stewart et al.

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

<.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 (

). 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 (

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) (

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 (

<.0001). Similarly, correlation in hospital inpatient days between both systems was significant (

<.0001). Back surgery was highly correlated between both systems (

<.0001); however, hip surgery was not significantly related. 

The highest spending Medicare markets were spread out geographically and included Miami, Florida, and McAllen, Texas (

). These markets did not rank highly in spending within the MHS. For instance, Manhattan was the third highest-spending HRR within Medicare, but ranked 276th (of 306 HRRs) within the MHS (Table 3). High-spending HRRs within the MHS included Washington, DC, and San Antonio, Texas—areas with a significant direct care footprint but lower Medicare rank (Table 3). Notably, there was greater similarity in low-spending HRRs. For instance, Dubuque, Iowa, ranked 305th in Medicare spending and 307th in the MHS. Lower-spending markets in both systems were located mostly in the Midwest (Table 3). 

An examination of healthcare resources between top- and bottom-spenders in both systems revealed consistent and anticipated patterns. In both systems, the top-spending HRRs were more heavily populated and had a comparable number of acute care beds per capita and a smaller ratio of primary care providers to specialty providers per capita. The relationship of this ratio between top and bottom spenders was consistent with the documented positive correlation between the number of specialists and more costly care (

Geographic variation in healthcare spending and utilization has gained a renewed focus in the climate of healthcare reform.

 In an attempt to elicit underlying causes for the unexplained variation, prior studies have compared Medicare spending with commercial healthcare markets

 Our study compared Medicare with the MHS, with a similar pricing scheme, in order to focus on the impact of utilization on healthcare costs.

Using age-, gender-, and race-adjusted data, we showed that variation of spending and utilization were higher in the MHS than Medicare. Higher variation in the MHS may have reflected inherent variability in the health status of the more demographically diverse MHS population. Additionally, the higher variation in the MHS may be partially explained by unobservable differences in patterns of care between the direct- and purchased-care sectors. In the Medicare cohort, the CoV for hip surgery was lower than that of back surgery, which is consistent with the fact that lower variation procedures typically tend to be nondiscretionary and physicians generally agree on the best treatment strategy.

 In the MHS population, however, the CoV for hip surgery was higher than that of back surgery, which is likely due in part to the very low rate of hip surgery in this population (approaching 0), which can drive up CoV values. 

Medicare sets prices for hospital reimbursement; however, in the commercial sector, prices are derived from negotiations between insurers and providers in a process influenced by competition between these 2 groups. Like Medicare, administrative pricing is also utilized in MHS for purchased care and is related by law.

 Therefore, price is less likely to explain the higher spending in Medicare compared with MHS, which is also supported by the higher degree of variation in utilization in both systems compared with the degree of variation in spending. Although the pricing schemes in the 2 systems are related, justifiable deviation from standardized indices is permitted.

 This may have resulted in subtle differences in how prices were set that may have allowed the MHS to take advantage of provider competition more effectively than Medicare and, consequently, reduced overall spending. These differences may stem from the difficulty in determining the appropriate price when actual costs are unknown, fluctuate over time, reflect discretionary provider culture, or are manipulated to benefit certain areas.

 For instance, if the MHS is more competitive in certain regions, the impact of provider market power on how prices are set in that region may be limited, potentially contributing to lower prices and decreased spending. 

The lack of positive correlation between commercial and Medicare spending across the United States has been partially attributed to administrative pricing by Medicare that is absent from the commercial healthcare market.

 This would imply that although variation in quantity of care is the main driver of spending in Medicare, variation in the privately insured market is largely driven by variation in provider prices, which is associated with hospital market power.

 We demonstrated a moderate direct correlation between Medicare and MHS spending after adjusting for age, gender, and race in both populations. This likely reflects similarity in pricing schemes between both systems. Likewise, utilization measures of inpatient days and back surgery were moderately significantly correlated; however, the hip surgery measure was not

The positive correlation between spending and certain measures of utilization may suggest the strong impact of local provider culture on utilization. Spending and hospital inpatient days have the same degree of correlation between both systems (0.3 and 0.29, respectively), likely reflecting the significant contribution of inpatient hospital care to total healthcare costs, which is supported by an intra-system assessment showing that correlation of spending and inpatient days within both systems was highly correlated (MHS: 0.5; Medicare: 0.6).

Despite moderate correlation in spending and utilization, we did not find a strong geographic pattern when we examined top-spending HRRs in Medicare and the MHS. McAllen, Texas—well known for high healthcare spending

—appeared as a top spender in our Medicare analysis. It ranked 141st of the 306 HRRs in the MHS. Similarly, although Takoma Park, Maryland, ranked high in the MHS population, it ranked 99th in Medicare. A simple explanation could be that high utilizers in each system tended to be located in different geographic areas and that local practice culture varied with the insurance market, forcing providers to adjust levels of utilization based on the predominant insurer in the region. Lower spending markets in both health systems appeared to be concentrated in the Midwest, suggesting that the local culture of utilization by providers had a larger impact in that region regardless of the insurer. The composition of the physician workforce may also impact the degree of spending, regardless of the insurer, as shown by the larger number of specialists located in top-spending MHS and Medicare markets compared with lower-spending markets. Although this finding was demonstrated previously in the Medicare population, the association between population concentration and quality of care delivery was less certain.

We showed higher variation within the MHS compared with Medicare, using HRRs as the unit of analysis. Although this examination of the MHS was novel, there were limitations to our study. Following the Dartmouth Atlas methodology, we did not adjust for health status, which may explain some of the variability in our study. Future studies should compare spending and utilization within the MHS to commercial healthcare, given the similar health profiles in these populations. We also did not assess price directly; however, TRICARE generally follows Medicare reimbursement levels and methods for purchased care.

 In addition, we did not assess market resources, such as physician and hospital capacity, which may impact administrative pricing, or study outcomes of care, which could justify higher spending in certain locations. Although we combined spending from the direct and purchased sectors of care within the MHS to calculate total costs, the individual costs for these 2 sectors were derived differently. Direct sector costs are patient-level cost allocations, and purchased sector costs are claims-based reimbursements. Thus, although purchased-care sector costs generally follow Medicare rules, direct-care sector costs do not, and this may impact comparisons of cost between the MHS and Medicare. Finally, we assessed differences in variation using correlation, preventing our ability to comment directly on causal relationships.

In comparing 2 systems with similar pricing schemes, differences in spending likely reflected variations in utilization and the influence of local provider culture. Our analysis found greater variation in the MHS compared with Medicare. It seems that unwarranted variation, largely attributable to utilization, is an issue deserving of further research to improve system performance through enhanced coordination and integration and to inform future managed care support contract negotiations. Research exploring the MHS compared with a more similar beneficiary demographic, such as the commercial insurance market, may be useful in this endeavor.

 Center for Surgery and Public Health, Department of Surgery, Brigham and Women’s Hospital (TA, SRL, LLN, JSW), Boston, MA; Uniformed Services University of the Health Sciences (LGK, SO), Washington, DC; The Dartmouth Institute (PG), Hanover, NH.

 Department of Defense, Uniformed Services University, Grant HU001-11-1-0023. The Henry Jackson Foundation grant funds this research.

 Dr Kimsey is an employee of the Department of Defense and is on active duty. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

 Concept and design (TA, LGK, JSW, SRL); acquisition of data (TA, LGK, PG, JSW); analysis and interpretation of data (TA, LGK, LLN, PG, JSW, SRL); drafting of the manuscript (TA, LGK, JSW, SRL); critical revision of the manuscript for important intellectual content (TA, LGK, LLN, PG, SO, SRL); statistical analysis (TA, LGK, SRL); obtaining funding (LGK, LLN, SO); and supervision (JSW). 

 Taiwo Adesoye, MD, MPH, Brigham and Women’s Hospital, 1620 Tremont St, 4-020, Boston, MA 02120. E-mail: taiwo.adesoye@gmail.com. 

1. Skinner J, Fisher ES. Reflections on geographic variations in U.S. health care. The Dartmouth Atlas of Health Care website. http://www.dartmouthatlas.org/downloads/press/Skinner_Fisher_DA_05_10.pdf. Published March 31, 2010. Updated May 12, 2010. Accessed December 17, 2015. 

2. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in Medicare spending. part 1: the content, quality, and accessibility of care. 

Articles

WEB EXCLUSIVE

Geographic variation in healthcare spending and utilization within the Military Health System is higher and significantly correlated with Medicare across hospital referral regions.

Geographic Variation in Medicare and the Military Healthcare System

 To evaluate the effects of formulary restrictions on utilization and costs of oral hypoglycemic agents (OHAs), statins, and renin-angiotensin system (RAS) antagonists among low-income subsidy (LIS) recipients in Medicare Part D plans.

We analyzed a 5% sample of 2012 Medicare data from the Chronic Conditions Data Warehouse together with a customized dataset capturing beneficiaries’ histories of plan assignment. 

We constructed 3 nonexclusive study cohorts comprising of users of OHAs, statins, and RAS antagonists. Eligible study subjects were LIS recipients randomized to benchmark plans. Formulary restrictions of interest were noncoverage, prior authorization, and step therapy. Study outcomes included generic dispensing rate (GDR), mean cost per prescription fill, and medication adherence based on proportion of days covered (PDC). Random intercept regression models were performed to estimate the effects of formulary restrictions on the study outcomes by drug class. 

After covariate adjustment, beneficiaries who were subject to formulary restrictions on brand name pioglitazone and single-source brand name dipeptidyl peptidase-4 inhibitors (saxagliptin, sitagliptin, and sitagliptin-metformin) had a GDR 3 percentage points higher and a cost per prescription fill $10.8 less, but similar PDC compared with those who faced no restrictions. Restricting access to brand name atorvastatin and single-source brand name statins (rosuvastatin and ezetimibe-simvastatin) was associated with a GDR 14.9 percentage points higher and a cost per prescription fill $29.6 less, but with no impact on PDC. Restricting use of single-source brand name RAS antagonists (olmesartan, valsartan, and valsartan-hydrochlorothiazide) was associated with a GDR 15.0 percentage points higher, a cost per prescription fill $27.2 less, and a PDC 1.3 percentage points lower. 

Placing formulary restrictions on brand name drugs shifts utilization toward generic drugs, lowers the overall cost per prescription fill, and has minimal impact on overall adherence for OHAs, statins, and RAS antagonists among LIS recipients.

Formulary restrictions—noncoverage, prior authorization, and step therapy&mdash;are increasingly employed by Medicare Part D plans to manage medication utilization and costs. This study evaluated the effects of formulary restrictions on utilization and costs of oral hypoglycemic agents (OHAs), statins, and renin-angiotensin system (RAS) antagonists among randomized low-income subsidy (LIS) recipients. 

Benchmark Part D plans tend to make generic drugs readily available on formulary and place restrictions on brand name drugs.

Formulary restrictions on brand name drugs shift utilization toward generic drugs, lower cost per prescription fill, and have minimal impact on overall adherence for OHAs, statins, and RAS antagonists among LIS recipients.

More than 30 million Medicare beneficiaries obtain prescription drug coverage through Part D each year.

 Although CMS requires Part D plans to include at least 2 drugs from each therapeutic class on their formulary, coverage of specific drugs may differ across plans. In 2010, some plans covered all drugs from the CMS drug reference file whereas other plans included as few as 63% of those drugs.

 Variation in coverage occurred not only to multisource brand name drugs, but also to single-source medications widely used by Medicare beneficiaries. In 2012, 5 of the 10 most frequently prescribed brand name drugs among Medicare beneficiaries were unavailable or excluded from formulary for more than 5% of all beneficiaries enrolled in standalone prescription drug plans (PDPs).

 Furthermore, use of prior authorization (PA) among covered drugs increased from 8% in 2007 to 20% in 2012.

Formulary noncoverage and utilization management (UM) tools (eg, PA, step therapy [STEP]) are intended to facilitate the efficient use of medications and contain prescription drug costs. Under the Medicare Part D program, an enrollee, an enrollee’s representative, or an enrollee’s prescriber can request that the plan sponsor make a formulary exception or determine whether the enrollee has satisfied a UM requirement. This process is called coverage determination.

 If a plan sponsor issues an unfavorable coverage determination, the decision may be appealed through the Part D appeals process. In the standard process, the adjudication timeframe is 72 hours for a coverage determination and 7 days for the first appeal.

 Prior research suggests that many beneficiaries, particularly those with low socioeconomic status, have difficulty navigating their Part D benefit and understanding the coverage determination and exception process.

 These challenges may present hurdles for beneficiaries to obtain and adhere to the medications prescribed to them. However, little is known regarding the impact of noncoverage, PA, and STEP—collectively referred to as "formulary restrictions" in this paper&mdash;on medication utilization and costs and whether those formulary restrictions may cause unintended reduction in overall medication adherence.

Two analytic challenges exist in bridging this knowledge gap. First, plans often impose formulary restrictions—either together or complementarily with cost-sharing rules&mdash;to control medication utilization and costs; thus, it is analytically complex to isolate the effects of one from the other. Second, factors that influence beneficiaries’ selection of PDPs (eg, health beliefs, risk preferences, and expectations for medication needs) are typically unobservable, but may bias estimates of plan effects.

Our study addressed these 2 analytic challenges by exploiting a natural experiment in which CMS randomly assigned beneficiaries receiving low-income subsidies (LIS) to benchmark PDPs within regions.

 More than 90% of all LIS recipients received a full subsidy and paid the same nominal co-pays for prescription drugs regardless of plan cost-sharing rules.

 Hence, focusing the analysis on LIS recipients allowed evaluation of the effects of formulary restrictions, independent of plans’ cost-sharing rules. To further ensure that LIS recipients had affordable prescription drug coverage, CMS randomly assigned those who did not voluntarily select a Part D plan to one of the benchmark PDPs in their region. Each year, CMS also reassigns LIS recipients from plans that are terminating or charging premiums above regional benchmark thresholds.

 Randomization is expected to balance both observable and unobservable beneficiary characteristics among plans, thus creating a unique research opportunity for understanding how formulary restrictions affect medication utilization and costs.

Our study focused on 3 classes of medications commonly used by Medicare beneficiaries: oral hypoglycemic agents (OHAs), statins, and renin-angiotensin system (RAS) antagonists. Adherence outcomes for these 3 drug classes were included as quality measures in the Medicare Star Ratings program and assigned the highest weight in calculating overall Stars. Recent CMS investigations have revealed that receipt of LIS is associated with poorer outcomes for several Star measures, including suboptimal adherence for OHAs, statins, and RAS antagonists.

 Findings from this study can also provide Part D plans with valuable insights regarding the extent to which lifting formulary restrictions may improve LIS recipients’ medication adherence for the 3 drug classes.  

We analyzed a 5% sample of 2012 Medicare data from the Chronic Conditions Data Warehouse (CCW). The CCW formulary file provided detailed information regarding formulary restrictions for the drugs of interest in 2012. The CCW prescription drug event file contained claims for prescription drugs incurred by beneficiaries during the year, as well as information on Part D plan characteristics and beneficiaries’ utilization of inpatient, outpatient, and physician services. More details about the CCW data files can be found elsewhere.

 In addition, we obtained a customized dataset from CMS that captured beneficiaries’ histories of plan assignment, dating back to 2006. Through this file, we were able to identify LIS recipients who were randomly assigned to benchmark PDPs by CMS and who remained in their assigned plan as of January 1, 2012.

We constructed 3 nonexclusive study cohorts comprising users of OHAs, statins, and RAS antagonists. Eligible study beneficiaries must have been continuously enrolled in Medicare Parts A, B, and D throughout calendar year 2012 and have received LIS throughout 2012. In addition, eligible beneficiaries for a particular cohort must have had at least 2 prescription fills for the drugs of interest included in the study in 2012. We excluded beneficiaries who made plan choices prior to 2012, but allowed those who switched plans in mid-year 2012 to remain in the data analysis according to the intention-to-approach commonly used in clinical trials. For technical reasons detailed below, we also excluded beneficiaries enrolled in 1 Part D contract (ie, Envision RxPlus Silver; Twinsburg, Ohio) from the analysis.

The 3 outcome measures were generic dispensing rate (GDR), mean cost per prescription fill, and medication adherence based on the proportion of days covered (PDC) by any drugs in the class. GDR was calculated as annual days of supply for generics divided by annual total days of supply for all drugs in the class. Mean cost per prescription fill was determined by dividing the total drug expenditure by the total prescription fills for all drugs in the class. PDC was computed by dividing days covered by total observation days. Days covered by overlapping prescriptions for the same drug were adjusted by rolling over the overlapping days to cover later days when lack of drug supply was identified. Similarly, days covered during inpatient stays were applied to subsequent days with no drug supply. Days in inpatient facilities were removed from both the numerator and the denominator of PDC.

In our assessment of formulary restrictions, we only considered drugs accounting for at least 1% of overall utilization of the drug class in all LIS recipients. Among those drugs, the scope of analysis was further narrowed down to those with varying degrees of restriction across the benchmark PDPs in 2012. The rationale behind these 2 requirements was that the impact of formulary restriction of a specific drug cannot be detected unless the drug was prescribed in the study population and its access was restricted to different degrees across plans.

Formulary restrictions of interest were noncoverage (ie, off formulary), PA, and STEP. To identify off-formulary medications, we first identified all drug products from the drug lists used by the Medicare Star Ratings program and then cross-checked to see exclusions to each plan.

 Indicators for PA and STEP were available from the CCW formulary file. We measured the degree of restriction for each drug identified at the brand level versus the generic and dosage-form levels, which allowed us to examine how restricting access to the brand name version of a multi-source drug or specific dosage forms might impact study outcomes. The degree of restriction for a drug was categorized as: 1) none restricted, if no strengths of that drug were subject to noncoverage, PA, or STEP; 2) partially restricted, if at least 1, but not all, strengths of the drug were subject to noncoverage, PA, or STEP; and 3) all restricted, if all strengths of the drug were subject to noncoverage, PA, or STEP.

Notably, we observed perfect correlations in the degree of restriction among drugs in each of the 3 classes. Specifically, every plan that partially restricted generic simvastatin also partially restricted ezetimibe-simvastatin. Plans either fully restricted brand name pioglitazone, sitagliptin, and sitagliptin-metformin across the board or did not restrict any of the 3 drugs. Plans that fully restricted valsartan also fully restricted valsartan-hydrochlorothiazide. We constructed composite measures that grouped commonly occurring formulary restrictions and estimated the effects of each group on the study outcomes. 

 provides a list of drugs selected for analysis, the degrees of restriction observed, and groups of formulary restrictions formed (eAppendix Table 1 displays formulary restrictions on individual drugs [

]). As indicated in the table, 3 OHAs (sitagliptin, sitagliptin-metformin, saxagliptin), 2 statins (ezetimibe-simvastatin, rosuvastatin), and 3 RAS antagonists (olmesartan, valsartan all, valsartan-hydrochlorothiazide) were single-source drugs for which generics were unavailable in 2012.

Key covariates included a variable for count of generics available without charge via mail order, indicators for PDP regions, and a categorical variable representing state laws for generic substitution. Plan cost-sharing rules are generally not applicable to full LIS recipients as they pay flat co-pays for prescription drugs (at most $2.60 for generics and $6.50 for brand name drugs in 2012).

 However, in 2012, several plans provided free mail order prescriptions for commonly used generics in the 3 drug classes of interest. The covariate—the count of generics available for free via mail order&mdash;was developed to account for the effect of this practice at the plan level. Last, several PDP regions covered multiple states, some of which could have had more forceful state laws for generic substitution than others. To represent the strictness of the state laws, we constructed a categorical variable consisting of 3 values: 1) state law allows generic substitution, 2) state law mandates generic substitution but allows brand by patient request, and 3) state law mandates generic substitution but allows brand by provider request.

We also evaluated the impact of CMS assignment methods by comparing enrollee characteristics across the benchmark PDPs within each of the 3 largest PDP regions: New York, Texas, and California. We expected comparable enrollee characteristics between plans, except for assignment year and age because some plans had operated as benchmark plans for more years and, thus, would have received more random assignees compared with plans that achieved benchmark status in recent years. These older benchmark plans would also have retained an older pool of enrollees. Beneficiary age and assignment year were also included as covariates in the analysis.

In univariate analysis, we performed type 3 tests to examine the overall effect of the composite groups of formulary restrictions on each study outcome. We estimated 3 sets of random intercept regression models in which dependent variables were GDR, mean cost per prescription fill, and PDC. In each regression model, we included a random effect to account for unexplained variability between plans. Formulary restrictions were modeled as fixed effects. Count of generics available without charge via mail order, state law for generic substitution, PDP region, assignment year, and beneficiary age were included as covariates in the models and treated as fixed effects. We used SAS Version 9.3 (SAS Institute; Cary, North Carolina) for all statistical analyses.

A total of 28,082 beneficiaries were eligible for the OHA cohort; 53,864 for the statin cohort; and 57,289 for the RAS antagonist cohort (see eAppendix Figure for details about cohort selection). Approximately 30% of all study subjects resided in New York, Texas, and California in 2012 (eAppendix Table 2). As expected, enrollee characteristics were largely comparable across benchmark PDPs within each of these 3 regions except for assignment year and age (eAppendix Tables 3-11).   

From our assessment of the benchmark PDP formulary designs in 2012, we found consistent patterns in formulary restrictions for OHAs, statins, and RAS antagonists. For most of the drugs analyzed, plans either fully restricted their use (all strengths restricted) or applied no restrictions at all (all strengths available). Formulary restrictions were mostly placed on brand name drugs, whereas almost all generic drugs were readily available on formulary. In addition, the benchmark PDPs appeared to have 3 formulary approaches for handling brand name drugs (

). From most restrictive to most generous, these directives were: 1) placing restrictions on all brand name drugs, 2) selectively covering brand name drugs without restrictions, and 3) covering all single-source brand name drugs and commonly used multi-source brand name drugs without restrictions.

The top panels in Tables 2-4 present descriptive statistics for annual days of supply for every drug that accounted for at least 1% of overall utilization in the LIS population, beginning with OHAs (Table 2), statins (Table 3), and RAS antagonists (Table 4). These statistics are displayed by the groups of formulary restrictions described in Table 1. Placing formulary restrictions on a drug was associated with lower utilization of that medication, and the impact was more pronounced among statins and RAS antagonists than among OHAs. 

Utilization of generic drugs was much higher among beneficiaries enrolled in plans that restricted access to brand name drugs. Compared with those enrolled in plans that placed no formulary restrictions on the 4 statins under study (Table 3), beneficiaries who faced restrictions in obtaining rosuvastatin (single-source brand name), atorvastatin (multi-source brand name), and ezetimibe-simvastatin (single-source brand name) not only had considerable fewer annual days of supply for the 3 drugs—rosuvastatin (9.10 for enrollees in plans with restrictions vs 35.41 for patients in plans with no restrictions), brand name atorvastatin (1.61 vs 20.52, respectively), and ezetimibe-simvastatin (1.13 vs 7.99)&mdash;but they also had higher use of generic atorvastatin (58.99 vs 45.86), generic lovastatin (21.27 vs 16.66), generic pravastatin (48.72 vs 39.21), and generic simvastatin (136.30 vs 120.77). Similarly, beneficiaries who were subject to restrictions in accessing single-source brand name angiotensin II receptor blockers (ARBs), including olmesartan, valsartan, and valsartan-hydrochlorothiazide, had more days of supply for generic ARBs versus patients in plans not requiring plan approval (losartan: 50.18 vs 27.91, respectively; losartan-hydrochlorothiazide: 14.89 vs 8.57, respectively) (Table 4).

The bottom panels of Tables 2-4 present mean values for each of the study outcomes. The mean GDRs for the 3 OHA restriction groups varied from 0.83 for plans with no restrictions to 0.84 for plans restricting only saxagliptin (single-source brand name) and 0.88 for plans restricting brand name pioglitazone and single-source brand name dipeptidyl peptidase-4 (DPP-4) inhibitors (ie, saxagliptin, sitagliptin, and sitagliptin-metformin). The mean GDR for statins was also lowest for plans with no formulary restrictions (0.77), climbing to 0.95 for plans placing restrictions on brand name atorvastatin and single-source brand name statins (ie, rosuvastatin and ezetimibe-simvastatin). For RAS antagonists, plans with no restrictions again exhibited the lowest mean GDR (0.80), with the highest mean GDR (0.95) observed among plans restricting single-source brand name ARBs (eg, olmesartan, valsartan, and valsartan-hydrochlorothiazide). Mean costs per prescription fill were inversely related to GDR. The range for OHAs was $54.54 in plans with the most restrictions to $71.70 in plans with no formulary restrictions for the 4 OHAs under study. The range for statins was $40.49 to $73.04, respectively, and for RAS antagonists, $20.78 to $45.74, respectively. The differences in PDCs across plans by formulary restriction were small; in no instance was the difference greater than 0.04.  

These relationships persisted after covariate adjustment for assignment year, beneficiary age, count of generics available for free via mail order, PDP region, and state law for generic substitution (Table 5). Regarding OHAs, restricting the use of brand name pioglitazone and single-source brand name DPP-4 inhibitors was associated with a GDR that was 3.0 percentage points higher (

<.0001) and a cost per prescription fill that was $10.80 lower (

= .0001) for OHAs. Restricting access to brand name atorvastatin and single-source brand name statins was linked to a GDR that was 14.9 percentage points higher (

<.0001) and a cost per prescription fill that was $29.60 lower (

<.0001) for statins. Restricting single-source brand name statins was associated with a cost per prescription fill that was $25.60 lower (

= .0158), and restricting brand name atorvastatin and single-source brand name ezetimibe-simvastatin was related to a reduction of $12.40 (

= .0399). Placing restrictions on single-source brand name ARBs was related to a GDR that was 15.0 percentage points higher (

<.0001) and a cost per prescription fill that was $27.20 lower (

<.0001) for RAS antagonists. Restricting only olmesartan was linked to a GDR that was 3.8 percentage points higher (

The estimated effects of formulary restrictions on overall adherence were minimal. Restricting access to brand name pioglitazone and single-source brand name DPP-4 inhibitors was linked to a PDC that was 0.4 percentage points higher (

= .3508), but the association was not statistically significant. Beneficiaries facing formulary restrictions of brand name atorvastatin and single-source brand name statins, on average, had a PDC that was 0.9 percentage points lower (

= .1197) compared with those that were not subject to such restrictions. Restricting the use of single-source brand name ARBs was related to a PDC that was 1.3 percentage points lower (

Stringent state laws for generic substitution (mandating generic substitution and only allowing brand name drugs by provider request) were associated with a GDR that was 3.3 percentage points higher for OHAs (

= .0120) and 3.0 percentage points higher for RAS antagonists (

= .0091). Providing free mail order prescriptions for 1 additional generic statin was associated with a GDR that was 1.5 percentage points higher (

We found that placing formulary restrictions on brand name drugs generally shifted utilization away from brand name agents toward inexpensive generics. The effects were consistent across the 3 drug classes, but the magnitude of impact was smaller for OHAs than for statins or RAS antagonists. LIS recipients who faced restrictions in accessing brand name drugs had 34.9% (38.80 vs 25.25 days) fewer annual days of supply for sitagliptin than their counterparts who could freely access these drugs, 74.3% (34.41 vs 9.10 days, respectively) fewer days of supply for rosuvastatin, and 95.7% (28.06 vs 1.22 days, respectively) fewer days of supply for valsartan (Tables 2-5). Correspondingly, the difference in GDR between the restricted and unrestricted groups was smaller for OHAs (0.88 vs 0.83, respectively) than for statins (0.95 vs 0.77) and RAS antagonists (0.95 vs 0.80). 

This varying magnitude of impact might be due to differences in the availability of therapeutically equivalent agents. There were multiple generic statins and generic ARBs available in 2012, whereas all DPP-4 inhibitors were single-source brand name drugs. Clinical guidelines on diabetes management recommend adding a DPP-4 inhibitor, sulfonylurea, or thiazolidinedione (TZD) as a second oral agent for patients who cannot achieve their glycemic target with metformin monotherapy.

 We observed that beneficiaries who either did or did not face restrictions on DPP-4 inhibitors had comparable utilization of sulfonylureas and TZDs, indicating lack of substitution. Although the findings from the regression analysis suggested that restricting access to DPP-4 inhibitors has minimal impact on overall OHA adherence, further study is needed to investigate concurrent adherence among patients treated with multiple OHAs and the clinical impact of formulary restrictions.

This study addressed the intended and unintended consequences of formulary restrictions, an understudied but important research topic given the near-ubiquity of these tools in modern formulary management.

 The lack of literature is partially due to complexity in deciphering formulary designs, especially if a large number of drug products are involved. Most prior studies are limited to providing descriptive statistics regarding shares of restricted drugs or evaluating formulary policy changes for particular drugs.

 Our analysis of the benchmark for PDPs formularies contributed valuable insight in identifying meaningful formulary restrictions and understanding how they may influence medication utilization patterns. To the best of our knowledge, this is the first study that has systematically evaluated formulary restrictions and their impact on medication utilization and costs in the context of an entire drug class. It is a methodological advancement because utilization of a drug is not only influenced by its own ease of access, but also formulary availability of other agents in the same therapeutic class.

The study estimates for the effects of formulary restrictions have high internal validity. By focusing on the randomized LIS recipients, we were able to minimize potential bias by isolating the effects of formulary restrictions from that of cost-sharing rules and removing confounding effects of virtually all beneficiary characteristics we could observe—and, by extension, other unobserved factors that represented critical threats to internal validity in conventional observational studies. 

Additionally, LIS recipients represent about 40% of Part D enrollees, yet account for over 55% of total drug spending.

 This is a socially and economically disadvantaged population that often lives with disabilities and multiple chronic conditions. They tend to have worse health outcomes, yet significantly higher healthcare expenditures.

 Such disparities suggest the need to improve both quality and efficiency of care for this vulnerable population.

In 2012, full LIS recipients paid at most $2.60 for generics and $6.50 for brand name medications.

 We found that neither the availability of free generics via mail order nor generous coverage of brand name medications seemed to meaningfully affect LIS recipients’ overall adherence to OHAs, statins, and RAS antagonists. Hence, LIS recipients’ suboptimal adherence may be due to behavioral or clinical causes rather than costs or access issues.

 Part D plan sponsors and other healthcare decision makers may consider behavioral interventions to improve LIS recipients’ perceived importance of medication adherence and disease management skills.

This study has a few limitations. First, the study subjects were randomized to plans rather than varying formulary restrictions. Hence, the observed effects of formulary restrictions may be confounded by other plan-level policies that also affect medication utilization patterns. We observed that several benchmark PDPs provided free mail order prescriptions for commonly used generics, which would incentivize beneficiaries to take generics instead of brand name drugs and use more medications overall. We also observed that Envision RxPlus Silver did not offer 90-days-supply prescriptions in 2012, whereas all the other benchmark PDPs did. Conceptually, shorter length of drug supply per prescription fill presents more opportunities for filling prescriptions late and may lead to gaps in medication usage. To eliminate these 2 plan-level policies as alternative explanations for the observed effects of formulary restrictions on the study outcomes, we excluded beneficiaries enrolled in the Envision RxPlus Silver plan from all analyses and included a covariate for count of generics available for free via mail order in the regression models. In addition, we estimated random intercept regression models to recognize variability in unobserved plan attributes.

Second, our randomized cohorts of LIS beneficiaries were not strictly comparable from plan to plan within PDP regions. Because Part D regional benchmark thresholds are determined annually through a competitive bidding process, plans can lose or gain benchmark status from year to year. When a plan loses benchmark status, its random assignees would be reassigned to other plans (however, there is an exception when the plan premium is just above the benchmark threshold). Additionally, the LIS recipients were randomized at different times. This dynamic randomization process resulted in small differences in beneficiary assignment year and age. To mitigate the potential confounding effects of beneficiaries’ tenure in the plan and age, we included both assignment year and age as covariates in the regression analysis.

Readers should use caution in generalizing the findings of this study to non-LIS beneficiaries. Compared with non-LIS beneficiaries, LIS recipients tend to be younger, more socioeconomically disadvantaged, and more likely to live with disabilities and multiple chronic conditions. Nevertheless, both LIS and non-LIS beneficiaries in each plan faced the same formulary policies and appeal process for requesting coverage exceptions. Although the impact of formulary restrictions on the study endpoints may differ in magnitude, we expected the nature of the relationships to hold in non-LIS populations. Last, our study focused on existing users of OHAs, statins, and RAS antagonists. Future research may consider evaluating the impact of formulary noncoverage and UM tools on treatment initiation among individuals who are candidates for those chronic medications.

Placing formulary restrictions on brand name drugs shifts drug utilization toward generics and lowers cost per prescription fill, but has minimal impact on overall adherence for OHAs, statins, and RAS antagonists among Medicare Part D enrollees receiving LIS. 

 Department of Pharmaceutical Health Services Research (XS, BCS, SET, EMP), and The Peter Lamy Center on Drug Therapy and Aging (BCS), and Department of Epidemiology and Public Health (LSM), University of Maryland, Baltimore, MD; Information Products Group, Office of Information Products and Data Analytics, Center for Medicare and Medicaid Services (CAP), Baltimore, MD.

 This work was supported by a Young Investigator Adherence Award funded by the Pharmaceutical Research and Manufacturers of America (PhRMA) Foundation. The funder played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. The views expressed in this paper are those of the authors and no official endorsement by HHS or CMS is intended or should be inferred.

 Concept and design (XS, LSM, EMP); acquisition of data (XS, BCS); analysis and interpretation of data (XS, LSM, EMP); drafting of the manuscript (XS, LSM); critical revision of the manuscript for important intellectual content (XS, BCS, CAP, SET, LSM, EMP); statistical analysis (XS, LSM); obtaining funding (XS); administrative, technical, or logistic support (CAP); and supervision (BCS, CAP, SET).

Placing formulary restrictions on brand name drugs shifts use toward generics, lowers the cost per prescription fill, and has minimal impact on overall adherence for antidiabetes, antihyperlipidemia, and antihypertension medications among low-income subsidy recipients in Medicare Part D plans.

Impact of Formulary Restrictions on Medication Use and Costs

To assess the lifetime cost-effectiveness of intermittent, reversible vagal nerve blocking (via the implantable weight loss device vBloc) therapy versus conventional therapy as treatment for patients who are class 2 obese with diabetes and for those who are class 3 obese with or without diabetes, who have found pharmacotherapy and behavioral therapies ineffective, but are not prepared or willing to undergo current bariatric surgical options.

A cost-effectiveness model was designed to simulate weight loss, diabetes remission, and costs in patients with obesity undergoing vagal nerve blocking therapy versus conventional therapy.

 The model compared 2 treatment arms, vagal nerve blocking therapy and conventional therapy, and for each treatment arm included 4 health states based on body mass index (BMI) class. Using Monte Carlo simulation, patients entered the model one at a time and could transition between health states by experiencing BMI change. The model focused on change in BMI and diabetes remission as predictors of healthcare costs, health-related quality of life, and survival. Inputs for vagal nerve blocking effectiveness were obtained from the ReCharge trial; however, remaining inputs were estimated from published literature. Incremental cost-effectiveness ratios (ICERs) were evaluated in terms of cost per quality-adjusted life-year (QALY) gained.

 ICERs for vagal nerve blocking versus conventional therapy in patients who were class 2 and class 3 obese were estimated to be $17,274 and $21,713 per QALY gained, respectively. Sensitivity analyses showed results to be robust to reasonable variation in model inputs, with the upper limit of ICERs remaining below $30,000 for all sensitivity analysis scenarios assessed.

Vagal nerve blocking therapy provides a cost-effective alternative to conventional therapy in patients who are class 2 obese with diabetes and in those who are class 3 with or without diabetes.

	Vagal nerve blocking therapy is a minimally invasive alternative to conventional therapy for the treatment of obesity and is positioned for patients who have found pharmacotherapy and behavioral therapies ineffective, but are not prepared or willing to undergo current bariatric surgical options. 

		A lifetime cost-effectiveness analysis of vagal nerve blocking versus conventional therapy in patients with class 2 and class 3 obesity was conducted.

		Patients experienced incremental cost-effectiveness ratios less than $30,000 in base and sensitivity analyses, demonstrating vagal nerve blocking therapy to be cost-effective relative to conventional therapy. 

		Model design incorporated both body mass index class stratification and diabetes remission, and simulated cost and quality-adjusted life-year outcomes.

It is estimated that more than one-third (34.9%) of adults in the United States are obese, with 8.8% considered class 2 obese (body mass index [BMI] = 35.0-39.9) and 6.0% considered class 3 obese (BMI ≥40.0).

 Lifestyle modifications, in the form of changes in food intake and activity level, and pharmaceutical therapy provide modest weight loss and have been demonstrated to be effective for some patients.

 At present, bariatric surgery has proven to be the most effective treatment option for significant weight loss and improvement in health.

Although bariatric surgery has demonstrated clinical efficacy in treatment of morbid obesity, recent evidence suggests that only a small percentage of patients who qualify for and would benefit from bariatric surgery ever undertake this mode of therapy.

 According to the results of one study published in 2015 using the 2007 to 2012 National Health and Nutrition Examination Survey (NHANES) database, it was estimated that as many as 15% of US adults (32 million) may be eligible for bariatric surgery based on the 2013 Guideline for the Management of Overweight and Obesity in Adults.

 However, according to the American Society for Metabolic and Bariatric Surgery (ASMBS), in 2013, only 179,000 bariatric surgical procedures were performed—a figure that is significantly below the number of potentially eligible candidates.

Another consideration for bariatric surgery is the associated mortality over time, with 1-year mortality at 2.4% and 5-year mortality at 6.4% for US adults. Adjusted analyses found no significant association between bariatric surgery and all-cause mortality in the first year of follow-up and significantly lower mortality at 5 and 10 years of follow-up.

 The gap in adoption of surgical treatments presents an opportunity to evaluate other low-risk, well-tolerated, and minimally invasive weight loss treatments.

The unmet need in current weight loss options is also associated with significant economic cost burden. Between 1998 and 2006, the annual medical cost burden of obesity increased from 6.5% to 9.1% of annual medical spending in the United States.

 In 2008 US dollars, the economic burden of obesity in the United States was estimated to be $147 billion per year.

 The current treatment gap in obesity, as well as the growing economic burden, has created meaningful value in the development and economic assessment of low-risk, well-tolerated, and minimally invasive treatment options.

One such option is vagal blocking using electrodes implanted through laparoscopic surgery. The Safety and Efficacy of vBloc Therapy Delivered by the Maestro Rechargeable System for the Treatment of Obesity (ReCharge) trial, a multicenter, randomized trial, evaluated vagal nerve blocking (via the implantable weight loss device vBloc) therapy through a sham-control design. This type of therapy uses a unique intermittent, vagal nerve blocking method to affect the perception of hunger and fullness and is delivered by a pacemaker-like device.

 The device is implanted through minimally invasive laparoscopic surgery and uses electrodes to block neural signal transmission to the vagus nerve,

 which is known to play a key role in satiety, metabolism, and autonomic control in upper gastrointestinal tract function.

 Vagal nerve blocking therapy is a reversible procedure and one that is neither anatomy altering nor restricting.

 According to the ASMBS position statement, it has demonstrated statistically significant excess weight loss in the short term and a low incidence of severe adverse events (AEs) and revision.

Compared with more invasive bariatric alternatives, vagal nerve blocking therapy provides a durable and less invasive weight loss therapy option, with a strong safety profile.

 Other available minimally invasive weight loss procedures include gastric balloon and gastric banding. Relative to gastric balloon therapy, in which the device is removed at or within 6 months of placement,

 vagal nerve blocking presents a durable and longer-term treatment option. In addition, the safety and efficacy of balloon therapy beyond 6 months have not been established. Further, the intent of the gastric balloon therapy is to offer a replacement balloon following the required removal at 6 months, for a potentially indefinite length of time. Even though gastric banding presents a similar perioperative safety profile as a vagal nerve blocking device, it is associated with high rates of reoperation

 and late complications, such as band slippage and pouch dilation,

and in recent years, rates of use have declined. Among bariatric surgeries, the proportion of gastric banding surgeries decreased from 35.4% in 2011 to 14.0% in 2013.

The objective of this study was to assess the cost-effectiveness of vagal nerve blocking versus conventional therapy. The population of interest was defined as individuals who had not been successful with behavioral therapy or pharmacotherapy and who sought an alternative that was cost-effective, minimally invasive, and demonstrated favorable comparative safety. Findings presented in this paper add to the current literature by providing insight into the cost-effectiveness of a newer mode of obesity treatment (vagal nerve blocking therapy) versus conventional therapy.

This cost-effectiveness model was developed in TreeAge Pro 2014 (TreeAge Software, Inc; Williamstown, MA), and it was designed to compare the vagal nerve blocking device with conventional therapy in patients who are class 2 obese with diabetes and those who are class 3 with or without diabetes. The model contained 4 BMI health states: not obese (<30 kg/m

). Through Monte Carlo simulation methods, patients were modeled one at a time

 and transitioned between health states by experiencing BMI change based on the treatment arm. Patients were tracked throughout the model and accumulated costs and utilities associated with their BMI levels. At the end of the model, incremental cost-effectiveness ratios (ICERs) were presented as cost per quality-adjusted life-years (QALYs) gained.

The model compared 2 treatment arms—vagal nerve blocking and conventional therapy&mdash;over a lifetime horizon. As described previously, vagal nerve blocking therapy is a minimally invasive bariatric surgical option. Conventional therapy, in the form of lifestyle counseling, was selected as the comparator for vagal nerve blocking therapy, as candidates for this type of therapy were patients who had not been successful in losing weight under medical management (eg, pharmacotherapy) and were not willing or ready to undergo other bariatric surgical options. Other minimally invasive weight loss procedures include gastric balloon and gastric banding. However, balloon therapy was not included in the model as its safety and efficacy beyond 6 months had not been established. Gastric banding was excluded as a comparator because its use has decreased substantially in recent years.

The model was built on change in BMI and diabetes status as predictors of cost and health-related quality of life. Over the course of the model, patients could experience BMI change, diabetes remission, and mortality. Costs and utilities were associated with BMI, and over the course of the model, patients accumulated costs and utilities by remaining in, or transitioning from, various BMI levels.

Each treatment arm was associated with the 4 BMI health states previously mentioned. At model entry, patients undergoing vagal nerve blocking and conventional therapy were placed in initial BMI health states according to their baseline BMI, which was determined by mean class 2 and class 3 BMIs in the ReCharge trial. Experiencing change in BMI allowed patients to transition between BMI health states. Diabetes and diabetes remission were also incorporated in the model structure. At baseline, patients were assigned an initial diabetes status according to their initial BMI health state, based on the indication explored in the ReCharge trial. In subsequent model cycles, patients were assigned a probability of diabetes remission, which was a function of the BMI trajectory associated with the treatment arm. The vagal nerve blocking device model also incorporated device replacement, as well as revision surgeries associated with both initial and device replacement implantations.

The final health state was death, for which a probability derived from the literature was applied at the end of each cycle. Patients remained in the model until death or age 100, whichever occurred first. A schematic of the model health states and transitions are provided in Figure 1.

At model entry, patients were assigned ages from a sampled distribution (mean = 47; standard deviation [SD] = 10) and sent through the model one at a time to receive vagal nerve blocking and conventional therapy in separate but parallel simulations. The age distribution was selected to align with the ReCharge trial population.

This lifetime economic analysis demonstrates vagal nerve blocking therapy to be a cost-effective alternative to conventional therapy in class 2 and 3 obesity patients.

August 2017

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