Gerard Anderson, PhD

 Self-insured employers cover more people than Medicare, Medicaid, or direct purchasers of private insurance.This study examined the ability of self-insured employers to negotiate hospital prices and the relationship between hospital prices and employer market power in the United States.

 Repeated cross-section analysis of commercial claims.

 We used the US Census Bureau County Business Patterns data to estimate employer market power at the metropolitan statistical area (MSA)–year level and used the Truven Health MarketScan commercial claims to estimate mean hospital prices and price ratios at the MSA-year level (2010-2016). We calculated descriptive statistics for employer market power, mean hospitalization prices, and a case mix–adjusted price ratio measure during the study period and analyzed the 10 most concentrated labor markets. We estimated MSA-year–level ordinary least squares regressions of hospitalization price and the price ratio measure on employer market power.

 Large self-insured employers had concentrated market power in very few MSAs in 2016. The mean value of our employer market power measure was 62 for 2016, compared with the mean value of 5410 for hospital market power in the United States. Regression analyses find a slight relationship: A 1-point increase in employer market power was associated with a $6.61 decrease in the hospitalization price (mean = $20,813), but this result becomes statistically insignificant once the models control for hospital wages.

 Employer market power is low in most MSAs. Self-insured employers may consider building purchase alliances with state and local government employee groups to enhance their market power and lower negotiated prices for hospital services.

In most areas of the United States, self-insured employers lack market power to negotiate hospital prices.

There is no evidence that greater employer market power is associated with lower hospital prices in the employer-sponsored insurance markets.

Employer coalitions would need to enroll a considerable number of employers to have a sufficient market power to negotiate with hospitals.

Self-insured employers may consider building purchase alliances with state and local government employee groups to enhance their market power and lower negotiated prices for hospital services.

fty-five percent of Americans younger than 65 years receive their health insurance from their employers.

 Recently, hospital prices paid by employers have increased much faster than inflation and outpaced what public payers pay.

 The price differential paid by employers for hospital services increased from 10% above the Medicare rate to more than 100% above the Medicare rate in many metropolitan statistical areas (MSAs) between 2005 and 2019.

 If the differential between public- and private-sector prices continues to widen, it could continue to cause slower or negative effective wage growth for US workers, whose total compensation increases have been absorbed by more expensive health benefits.

 meaning that employers bear financial risk for their health plans. Previous research in this area has focused on how insurer concentration could lead to lower private-sector hospital prices.

 However, most insurers are paid through administrative services only (ASO) contracts, do not assume risk, and may not have a direct incentive for lower prices.

 Insurers may use their entire book of business and compete for potential clients (ie, self-insured employers) by offering them low-premium plans by obtaining low health care prices.

 However, insurers can also compete for employers based on administrative fees instead of prices, as prices are often not shared with employers during the bidding process. Furthermore, ASO contracts are sometimes paid as a percentage of overall claims,

 which would actually create a negative incentive to negotiate lower prices, or on a per-member per-month basis, which would attenuate the incentive.

Empirically, this has been borne out in the data. A recent National Bureau of Economic Research working paper by Craig et al has examined within-payer price variation at hospitals in Massachusetts,

 finding that those enrolled in ASO plans pay higher prices for the same services at the same hospital than those enrolled in fully insured plans administered by the same payer. This empirical evidence suggests that insurers, because they lack the incentive, may not be negotiating lower prices for their ASO enrollees. Although insurers benefit from having self-insured employers in their book of business,

 they may not want to alienate hospitals who are among the largest employers in the community and important potential customers.

The growing differential between Medicare and private-sector prices shows that the private health care payment system is not keeping pace with consolidation in the hospital sector.

 In response to this growing differential, some large self-insured employers—for example, Berkshire Hathaway, JPMorgan Chase, and Amazon—have created an entity to address the cost of their health care benefits, and some large self-insured employers have begun to directly negotiate prices with hospitals.

 However, the success of these efforts is likely to be a function of employers’ negotiating power in health care markets. On one hand, employers may solicit bids from a variety of insurers and work with consultants to choose insurers that pay lower prices. If this is the case, insurers should work to achieve low prices and employer negotiating power should not matter. On the other hand, large employers with significant market power may have some leverage against hospital systems. If ASO insurers are not negotiating lower prices, then an employer’s market power could function as a counterforce to provider and insurer market power (eg, the dominant hospital system may want to serve the dominant employer). These large employers may be able to use their market power in negotiations by engaging in direct contracting with providers, demanding that insurers negotiate lower prices, or to remove gag clauses that prevent the employers from seeing the prices up front. There is some evidence of this occurring both in the private sector

 The extent to which this is happening across the country in areas where employers do have market power is unknown, a priori.

In this study, we examined the extent of employer market power in the context of hospital price negotiation and explored whether employers can use their market power to obtain lower prices for hospitalizations. We used Truven MarketScan data to examine inpatient hospital prices across MSAs and constructed a measure of employer market power adapted from the labor economics literature.

 We examined the level of employer market power across MSAs in the United States, identifying markets with particularly high and low levels of employer market power, and examined the association between employer market power and hospital prices.

We used the US Census Bureau County Business Patterns (CBP) data to estimate employer market power at the MSA level for each year between 2010 and 2016.

 The CBP data provide annual statistics for all businesses with paid employees within the United States. At a county level, the CBP data provide estimates of the number of firms with the following ranges of employees: 1 to 4, 5 to 9, 10 to 19, 20 to 49, 50 to 99, 100 to 249, 250 to 499, 500 to 999, 1000 to 1499, 1500 to 2499, 2500 to 4999, and 5000 or more.

We used the Truven Health MarketScan commercial claims and annual enrollment data files, sourced from large employers, for the years 2010-2016 to estimate MSA-year–level prices.

 The Truven MarketScan data include information from 350 insurers offering private-sector employer-sponsored coverage to approximately 25% of all the commercially insured individuals and their families in the United States. This database has been used to estimate local-level hospital prices in prior research.

Additional MSA-year–level characteristics were obtained from the Current Population Survey (CPS), the American Hospital Association (AHA) Annual Survey, and the American Medical Association (AMA) Competition in Health Insurance annual reports. Demographic data and unemployment rates were obtained from CPS at the county level and were aggregated to the MSA-year level. Hospital concentration measures were obtained from AHA at the county level and aggregated to the MSA-year level. Insurer concentration data were available from the AMA reports at an MSA-year level. To adjust hospital prices for local wages in the hospital setting, we used the CMS wage index, a standardized measure of local hospital wages relative to national hospital wages.

 The CMS wage index is used to adjust prospective payments to hospitals based on local hospital wages and defines labor market areas by Core-Based Statistical Areas, which align with our MSA-year–level analyses.

Our key measure was employer market power. The employer market power measure was defined as the sum of the squared market shares in the labor market for each employer (similar to the Herfindahl-Hirschman Index [HHI]),

 following the literature in labor economics.

 In the labor economics literature, each industry is evaluated separately; however, we aggregated industries for our analysis because the industry is not relevant with respect to hospital prices and negotiations. To later match to the hospital price data, we constructed the employer market power measure at the MSA level. Because labor markets are generally smaller than an MSA, we constructed the employer market power measure at the county level and then took a weighted mean up to the MSA level. Our process followed 3 steps.

First, we estimated the number of employees within a county by assuming that each employer within a range (eg, 5-9) employs the midpoint of that range (eg, 7.5) and then summed the total employees across firms within a county. For employers reporting 5000 or more employees, we estimated the number of employees by subtracting the estimated number of employees in all the smaller categories from the total number of employees in the county, and then divided by the number of employers with 5000 or more employees.

Second, we took the estimates derived above and calculated a county-level measure of employer market power. A value close to 0 indicates a highly competitive labor market and a value of 10,000 indicates a perfectly monopsonistic employer.

Third, we took the weighted mean of these county-level measures at the MSA level, weighted by the employers’ market shares in that MSA, to calculate each MSA’s employer market concentration.

Our dependent variable was hospitalization price. We constructed 2 hospitalization price measures using the Truven Health MarketScan commercial claims data for inpatient admissions. First, we computed the mean inpatient hospitalization price for each MSA-year. To account for differences in case mix, we also computed a price ratio measure that controls for differences across markets. Specifically, the expected price in each MSA-year was calculated by multiplying the national prices for each diagnosis-related group (DRG) code by the number of cases for each DRG in that MSA. This price ratio measure accounts for differences in case mix that may otherwise drive differences in prices across markets by adjusting for the local frequency of DRG codes.

We calculated descriptive statistics, mapped the distribution of employer market power across the country, and analyzed the 10 most concentrated labor markets, focusing on the largest employer in each market. To analyze the descriptive relationship between inpatient prices and employer market power, we estimated MSA-year–level ordinary least squares regressions of hospital prices on employer market power. We estimated these models with year fixed effects to account for time trends, then added market-level controls, and finally added the CMS wage index to the regressions to examine the relative contribution of wages. We estimated models separately with a linear specification of employer market power and with the linear specification plus quartiles of employer market power. The nonlinear specifications are incorporated to test the hypothesis that employer market power is strongest at the highest levels of the distribution, as has been found in prior literature on employer monopsony power and wages.

 We also estimated log-transformed models as a sensitivity check.

We performed 3 key robustness checks. First, to account for the potential endogeneity of employer market power, we estimated an instrumental variable (IV) model where we instrumented MSA-level employer market power with the employer market power of the nearest MSA, identifying the nearest MSA based on the geographic distance between the centroids of MSAs.

 Second, because employers may have more bargaining power in more competitive hospital markets, we estimated a series of models where we interacted employer market power with hospital HHI. Third, markets with significant employer market power may also have a health care organization as the largest employer, potentially diminishing the association between employer market power and hospital prices. To examine this, we estimated models excluding all 819 (33.0%) MSA-years in which a health care organization was the largest employer.

, we show descriptive statistics related to the key measures of employer market power and prices over time. In 2016, the average employer market was very unconcentrated, with a mean (SD) of 61.59 (70.88) and median of 46.27. Overall, labor markets were becoming less concentrated over time, with a mean (SD) of 63.70 (63.37) in 2010 and 61.59 (70.88) in 2016.

The MSA-level mean (SD) hospitalization price in 2016 was $23,911 ($6789), with a median (interquartile range) of $22,377 ($19,777-$26,323), consistent with previous MSA-level estimates.33 The mean hospitalization price increased from $17,898 in 2010 to $23,911 in 2016. The price ratio measure, by nature, has a mean close to 1 (mean [SD], 0.98 [0.28] in 2016). In 2016, the price ratio measure ranged from 0.57 to 2.56 (ie, hospitalization prices 43% less than the national mean to 156% higher than the national mean, controlling for case mix).

, we show the geographic distribution of employer market power across the United States in 2016. Larger, more populous MSAs tended to have less concentrated markets, and smaller, less populous MSAs tended to have more concentrated markets.

 displays the MSAs with the 10 highest values of employer market power in 2016, along with select characteristics. Markets with relatively high levels of employer market power were generally smaller cities. The largest employers in the more concentrated MSAs spanned a range of industries but generally fell into 3 categories: large universities (Ithaca, New York; Blacksburg-Christiansburg-Radford, Virginia; Morgantown, West Virginia; and Lafayette-West Lafayette, Indiana); health systems (Rochester, Minnesota; Madera, California; Weirton-Steubenville, West Virginia-Ohio; and Winchester, Virginia-West Virginia); and manufacturing (Sioux City, Iowa-Nebraska-South Dakota; and Kokomo, Indiana). These concentrated MSAs generally had hospital prices lower than the mean but had a wide range: from $15,319 in Weirton-Steubenville, West Virginia-Ohio, to $30,140 in Morgantown, West Virginia. Adjusting the prices for the case mix in each MSA, the price ratio measure followed a similar trend. Detailed sources and methods underlying this table can be found in 

, the relationship between hospital prices and employer market power is examined. Panel A of Table 3 presents the relationship with a linear specification of employer market power. Columns 1 through 3 model the mean hospitalization price and employer market power, whereas columns 4 through 6 model the price ratio measure. With and without market-level controls, there was a statistically significant relationship between employer market power and hospitalization prices. Adjusting for market controls, including sex, race/ethnicity, education level, unemployment, insurer HHI, and hospital HHI, a 1-SD increase in employer market power (ie, a 65-point increase) was associated with a $430 lower mean hospitalization price. Accounting for differences in differences in case mix, a 1-SD increase in employer market power was associated with 2.1% lower prices. This relationship was substantially weaker and no longer statistically significant after controlling for the CMS hospital wage index, which has an extremely strong relationship with prices. Panel B of Table 3 shows the relationship between hospital prices and employer market power, with a nonlinear specification of employer market power that includes the continuous measure along with a categorical variable for the quartile of employer market power. We find qualitatively similar results. As a sensitivity check, we reestimated our models with log-transformed outcomes (

). The results did not qualitatively change.

, we present results from the IV model. In the first stage, we found a strong 

 statistic of 12.34. The second-stage results were not qualitatively different from the main results presented in Table 3.

 displays results interacting employer market power and hospital HHI. We found no statistically significant interaction between these 2 measures, with point estimates around 0.

, we examined how our results may change when excluding MSA-years in which a health care organization was the largest employer. Although the overall results were unchanged, the association between employer market power and hospital prices was slightly larger, yet still not statistically different from 0 once we controlled for hospital wages.

Our results showed that large, self-insured employers did not have concentrated market power in most MSAs. The mean value of employer market power was 62 for 2016, compared with the mean value of 5410 for hospital market power in the United States.

 Our regression analyses find a slight (but economically insignificant) negative relationship between employer market power and hospital prices when we estimate models without hospital wage controls, and this result becomes statistically insignificant once the models control for hospital wages. Given these 2 alternate specifications, we do not find evidence that employers are using bargaining power to negotiate lower hospital prices.

It has been suggested that employers should take an active role in negotiating prices with hospitals, rather than relying on ASO insurers to do so.

 Our study suggests that almost all employers, operating alone, simply do not have the market power to impose a threat of effective negotiation. Given that health insurers have not been effective in negotiating hospital prices, alternative approaches may be needed to constrain hospital prices for self-insured employers.

Instead of directly negotiating prices, some large self-insured employers have adopted other approaches to contain their health care spending, such as directing patients toward centers of excellence,

 improving incentives for in-network care, offering high-deductible health plans in combination with personal medical accounts,

 and changing drug formularies to encourage generic drug usage.

 However, the effectiveness of these tactics can be limited by local market hospital concentration, which forces employers to accept the prices that hospitals charge.

Creating purchasing alliances is another strategy for employers to increase their purchasing power.

 To be an effective negotiator, such purchasing alliances need to have substantial market power in their geographic area. In most MSAs, achieving substantial market power would require that many large employers would all agree on the same approach. Achieving this level of collective action may be a challenge; in many cities, the combined market share of major employers is relatively small. One approach that may hold promise is creating purchase alliances of private and public employee groups,

 which can take a more active role in negotiating with hospitals using their combined large market share.

This study had several important limitations. First, our analysis was not causal. Instead, we focused on the descriptive nature of employer market power and the descriptive relationship between employer market power and hospital prices. Second, we estimated our analysis at the MSA level, as the MarketScan data do not have finer geography. Although some prior studies have measured employer market power at the commuting zone–industry level,

 we believe that an MSA-level analysis is appropriate as it has been used in prior studies of hospital prices

 and because the employer’s total market share, not their market share within a geographic market-industry combination, should be key in price negotiations. Third, we were not able to observe which employers were specifically included in the MarketScan data. Although the MarketScan data are sourced from large employers, there could be markets in which the largest employer is not included. This may bias the descriptive relationship between employer market power and inpatient prices toward a null result of no relationship. However, in 7 of the 10 most concentrated markets, we found that MarketScan enrollment substantially exceeded the estimated number of employees at the largest employer, enhancing the credibility of our estimates.

This study examined inpatient hospital prices and employer market power across the United States between 2010 and 2016. Employer market power was low in most MSAs, a potential explanation for why employers historically have not taken a more active role in price negotiation. Employer coalitions would need to enroll a considerable number of employers to have a sufficient market power to negotiate with hospitals. Self-insured employers may consider building purchase alliances with state and local government employee groups to enhance their market power and negotiate lower prices for hospital services.

 Department of Health Policy and Management, Bloomberg School of Public Health (MDE, MKM, GB, APS, GA), and Carey Business School (GB), Johns Hopkins University, Baltimore, MD.

 This project was supported by Arnold Ventures and grant number T32HS000029 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.

 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.

 Concept and design (MDE, MKM, GB, APS, GA); acquisition of data (MDE, MKM, GA); analysis and interpretation of data (MDE, MKM, APS); drafting of the manuscript (MDE, MKM, GA); critical revision of the manuscript for important intellectual content (MDE, MKM, GB, APS); statistical analysis (MKM); obtaining funding (GA); administrative, technical, or logistic support (GB); and supervision (GA).

 Matthew D. Eisenberg, PhD, Bloomberg School of Public Health, Johns Hopkins University, 624 N Broadway, Rm 406, Baltimore, MD 21205. Email: eisenberg@jhu.edu.

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Articles

This study examines the ability of self-insured employers to negotiate hospital prices and the relationship between hospital prices and employer market power in the United States.

Large Self-insured Employers Lack Power to Effectively Negotiate Hospital Prices

 Stand-alone prescription drug plans (S-PDPs) and Medicare Advantage prescription drug (MA-PD) plans are incentivized to cover outpatient medications differently. This could affect the coverage of inhalers that prevent costly exacerbations of chronic obstructive pulmonary disease (COPD), with impacts for the Medicare program and its beneficiaries. This study compared the coverage of guideline-recommended COPD inhalers between S-PDPs and MA-PD plans.

 A cross-sectional analysis of the formularies for all 689 S-PDPs and 2578 MA-PD plans offered in 2017.

 We assessed each prescription drug plan’s coverage of inhalers in 6 therapeutic categories recommended by the 2017 Global Initiative for Chronic Obstructive Lung Disease (GOLD) report and compared the use of prior authorization, step therapy, and coinsurance between S-PDPs and MA-PD plans.

 In 2017, all S-PDPs and MA-PD plans covered at least 1 inhaler from each GOLD therapeutic category, except for long-acting β agonist/long-acting muscarinic antagonist combination inhalers. S-PDPs were more likely to require coinsurance for inhalers across all therapeutic categories, whereas MA-PD plans required prior authorization more frequently for 3 of the 6 therapeutic categories. S-PDPs required coinsurance more frequently than MA-PD plans for inhalers that treat mild (20.8% vs 11.4%; 

 < .001), and severe (45.4% vs 11.0%; 

 Medicare Part D S-PDPs are more likely than MA-PD plans to require coinsurance for outpatient COPD inhalers, especially for severe disease. This likely reflects their different financial incentives and is an important consideration for providers and policy makers aiming to improve outpatient COPD management.

Although almost all Medicare Part D plans covered guideline-recommended chronic obstructive pulmonary disease inhalers in 2017, the utilization controls that they used varied by plan type.

Medicare Advantage prescription drug (MA-PD) plans cover both prescription drugs and medical services, whereas stand-alone prescription drug plans (S-PDPs) provide only prescription drug coverage.

S-PDPs required coinsurance more frequently than MA-PD plans for all guideline-recommended therapeutic categories, whereas MA-PD plans required prior authorizations more frequently.

These formulary designs likely reflect the different financial incentives of Medicare Part D plans and could adversely affect the medication adherence of S-PDP beneficiaries and Medicare spending.

Three-fourths of Medicare beneficiaries obtain prescription drug coverage through the Medicare Part D Program.

 The program consists of stand-alone prescription drug plans (S-PDPs) and Medicare Advantage prescription drug (MA-PD) plans. S-PDPs are privately operated plans offered to beneficiaries who are enrolled in fee-for-service Medicare. MA-PD plans provide coverage for beneficiaries who are enrolled in MA health plans. In MA-PD plans, the insurer that offers prescription drug coverage also covers medical services, whereas in S-PDPs the prescription drug coverage is separate from medical coverage. As a result, MA-PD plans are responsible for the full range of medical costs that result from their drug cost containment strategy, whereas S-PDPs are not.

Previous studies have investigated whether these differing responsibilities affect the prescription drug coverage offered by plans.

 The concern is that S-PDPs are incentivized to offer more restrictive formularies than MA-PD plans. A plan’s formulary can be restricted by limiting the number of drugs covered, requiring prior authorization or step therapy for a drug, and charging higher cost sharing through the use of co-pays (patient pays a fixed cost per prescription) or coinsurance (patient pays a percentage of the medication’s list price). These strategies encourage the use of certain drugs over others, but they also impose barriers that limit a patient’s access to drugs, potentially reducing their medication adherence.

Huskamp et al evaluated the Medicare Part D formulary coverage of drugs for 6 common chronic diseases in the Medicare population.

 They found that for brand-name drugs with a generic alternative, MA-PD plans, on average, were more likely than S-PDPs to cover the drugs, were less likely to require prior authorization or step therapy, and charged a lower co-pay. These differences did not typically persist, however, when they evaluated only branded drugs without a generic equivalent.

The Medicare Part D coverage of branded drugs without generic equivalents is particularly relevant for chronic obstructive pulmonary disease (COPD). COPD accounts for about $20 billion of Medicare spending per year.

 As much as 70% of this spending is for COPD exacerbations, which frequently require hospitalization or an emergency department visit.

 One of the most effective methods for preventing COPD exacerbations is outpatient inhalers,

 which require good adherence to reduce the frequency and severity of exacerbations.

There are evidence-based guidelines directing the outpatient management of COPD.

 These guidelines recommend the use of daily maintenance inhalers for chronic disease management and the use of short-acting rescue inhalers for intermittent symptom control. A stepwise increase in therapy is recommended as the severity of a patient’s COPD worsens.

 Prior to 2019, all maintenance inhalers were branded drugs without generic equivalents.

In 2015, Tseng et al found that MA-PD plans covered maintenance inhalers more frequently than S-PDPs but that MA-PD plans required higher cost sharing for a majority of inhalers.

 This finding differed from those of Huskamp et al and aligned with findings from an evaluation of Medicare Part D plan coverage of disease-modifying antirheumatic drugs.

 However, Tseng et al calculated the cost-share attributable to coinsurance using the plan’s published price, which does not account for rebates, instead of the list price. This likely underestimated the effect of coinsurance and makes the comparison challenging to interpret.

The ability of maintenance inhalers to prevent expensive COPD exacerbations means that Medicare spending is lower when inhalers are covered and beneficiaries use them.This important question requires further analysis. In this study, we examine the coverage of guideline-recommended inhalers for COPD and barriers to accessing them in the Medicare Part D program, comparing between S-PDPs and MA-PD plans and across different severities of the disease.

Drugs of Interest: Defining the Study Cohort

Inhalers used for the management of COPD were identified using the 2017 Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines.

 The FDA National Drug Code Directory provided unique identification codes (National Drug Codes [NDCs]) for all commercially available preparations of all inhalers listed in the GOLD guidelines.

 Each NDC uniquely identifies a product according to its active ingredient, dosage form, strength, labeler, and packaging.

Inhalers were grouped into 6 therapeutic categories by mechanism of action according to the GOLD guidelines: (1) short-acting bronchodilators, (2) long-acting β agonists (LABAs), (3) long-acting muscarinic antagonists (LAMAs), (4) LABA/LAMA combinations, (5) LABA/inhaled corticosteroid (ICS) combinations, and (6) ICS-only inhalers. One additional drug class (LABA/LAMA/ICS combinations) was not included in our analysis because it contained a single product that was approved in late 2017.

Inhalers were also grouped according to the severity of the disease that they were recommended to treat based on the GOLD disease classification.

 Short-acting bronchodilators were classified as mild disease therapies (recommended for GOLD Group A disease). LABA and LAMA inhalers were classified as moderate disease therapies (recommended for GOLD Groups B and C disease). LABA/ICS combination and ICS-only inhalers were classified as severe disease therapies (recommended for GOLD Group D disease). All inhalers in the sample, with the exception of short-acting inhalers, were brand-name medications in 2017. Two independent adult pulmonologists with COPD expertise independently reviewed the list of drugs and the therapeutic classifications to confirm completeness and accuracy.

The Medicare Part D November 2016 Prescription Drug Plan Formulary, Pharmacy Network, and Pricing Information Files provide information on the plans and drug formularies offered by all S-PDPs and MA-PD plans operating in 2017. This database was used to extract all characteristics of the drugs of interest, identified by their NDCs. Plan enrollment information was obtained from the CMS Medicare Advantage/Part D Contract and Enrollment Data for the month of November 2016.

The Medicare Prescription Drug Plan Formulary, Pharmacy Network, and Pricing Information Files were used to identify which drugs were covered by each plan, whether or not the drug had a prior authorization or step therapy requirement, and the cost-sharing burden associated with each drug.

 Patient cost-sharing burden was assessed in 3 ways. First, the lowest cost-sharing tier in which plans offered an inhaler from each therapeutic category was identified. Higher tiers are typically more financially burdensome to patients.

 Second, the frequency of coinsurance for products in the same therapeutic category was calculated. Third, the therapeutic categories were grouped by the severity of disease that they are recommended to treat, and the frequency of coinsurance for inhalers in the same disease severity was calculated. Patient-level data were not evaluated.

Coinsurance requirements were assumed to be more burdensome for patients than fixed co-payments because coinsurance amounts vary depending on the price of the inhaler. All cost-sharing information was obtained assuming a 30-day supply of the inhaler at the plan’s preferred pharmacies. Medicare Part D plans typically distinguish between preferred and nonpreferred pharmacies and offer lower cost sharing at the preferred pharmacies. For plans without cost-sharing information for preferred pharmacies, nonpreferred pharmacy cost-sharing data were used instead. Plans with missing cost-sharing information for both preferred and nonpreferred pharmacies were excluded from the cost-sharing analysis. The Medicare cost-sharing structure has different rates depending on how much the specific beneficiary has spent on drugs, beginning with the deductible period, then the coverage period, gap period (“donut hole”), and catastrophic period. For consistency, cost-sharing information was collected in the initial coverage period for all plans.

Descriptive analyses were conducted separately for each of the 6 COPD inhaler therapeutic categories in terms of frequency of prior authorization and step therapy requirements, minimum cost-sharing tier, and frequency of coinsurance requirement. The characteristics were then compared between S-PDPs and MA-PD plans using χ

 tests for categorical variables and unpaired 2-tailed 

 tests for continuous variables. The characteristics were also compared by the 3 disease severity groups (mild, moderate, and severe) using simple linear regression models. A sensitivity analysis to test for the robustness of the results was performed by excluding plans that did not provide cost-sharing information for preferred pharmacies from the cost-sharing analyses. All statistical analyses were implemented using Stata statistical package version 15 (StataCorp). This project was approved by the internal review board at Johns Hopkins Bloomberg School of Public Health.

A total of 689 S-PDPs and 2578 MA-PD plans were offered to Medicare Part D beneficiaries in 2017. These plans covered 30.6 million Medicare beneficiaries, with 59% of enrollees covered by S-PDPs and 41% covered by MA-PD plans. A total of 117 (17.0%) S-PDPs and 1375 (53.3%) MA-PD plans did not provide cost-sharing information for preferred pharmacies and in this case, nonpreferred pharmacy cost-sharing information was used.

 Finally, 29 (1.1%) MA-PD plans provided no cost-sharing data and were therefore excluded from the cost-sharing analysis.

Part D plans, in aggregate, covered 70 different inhalers with 25 unique active ingredients or combinations of active ingredients in 2017 (

). All S-PDPs and MA-PD plans covered at least 1 drug in each of the 6 therapeutic categories, except for LAMA/LABA combination inhalers: 6% of S-PDPs and 3% of MA-PD plans did not cover any drugs in this therapeutic category. All plans did cover at least 1 LAMA-only and at least 1 LABA-only inhaler. S-PDPS offered fewer short-acting (10.8 vs 13.0; 

 < .001), LABA/ICS combination (9.3 vs 10.3; 

 < .001), and ICS-only (13.2 vs 14.5; 

 < .001) inhalers on average compared with MA-PD plans.

Utilization Controls: Frequency of Prior Authorization and Step Therapy

S-PDPs applied prior authorization less frequently than MA-PD plans for LABA (30.6% vs 35.9%; 

 = .005), and LABA/ICS (1.8% vs 3.1%; 

). S-PDPs applied prior authorization more frequently than MA-PD plans for ICS-only inhalers (22.2% vs 20.8%; 

 < .001). There was no statistically significant difference between S-PDPs and MA-PD plans in frequency of use of prior authorization for short-acting and LAMA inhalers. Both types of plans applied step therapy to less than 4% of inhalers per plan for all therapeutic categories, although MA-PD plans required step therapy more frequently than S-PDPs for short-acting (1.6% vs 0.3%; 

 < .001) and LABA/ICS (3.3% vs 1.6%; 

Patient Cost Sharing: Tier Placement and Frequency of Coinsurance

The minimum tier of coverage for short-acting inhalers was either tier 1 or tier 2 for all S-PDPs and MA-PD plans (

). MA-PD plans were more likely to use tier 1 compared with S-PDPs (45.8% vs 35.6%; 

Although most Medicare Part D plans cover guideline-recommended outpatient chronic obstructive pulmonary disease (COPD) inhalers, the utilization controls applied to these therapies vary by plan type.

Gerard Anderson, PhD

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