Inmaculada Hernandez, PharmD, PhD

 To evaluate how changes in generic drug prices and the incidence of abrupt price increases varied with the number of manufacturers supplying each drug.

 Analysis of 2005 to 2016 monthly wholesale acquisition costs (WACs) and University of Pittsburgh Medical Center Health Plan counts of pharmacy claims for National Drug Codes (NDCs) for generic drugs.

 Each year, NDCs were categorized according to the number of manufacturers offering each combination of active ingredient and dosage form: 1 to 3, 4 to 7, and more than 7. For every month from January 2006 to January 2017, we estimated the 12-month change in WAC (eg, 12-month change in January 2006 was calculated as the difference in WAC between January 2006 and January 2005, divided by the WAC in January 2005), before and after weighting each NDC by counts of pharmacy claims. We evaluated the proportion of NDCs that had large price increases, greater than 20%, 50%, 100%, and 500% within a year.

 Before 2010, price changes were higher for drugs supplied by a lower number of manufacturers; however, after 2010, prices increased sharply, and drugs supplied by 4 to 7 manufacturers showed increases similar to or higher than those supplied by 1 to 3. In 2013, prices increased by an average of 29% for drugs supplied by 1 to 3 and 4 to 7 manufacturers, and 10% for more than 7. Price changes increased after weighting by counts of pharmacy claims, demonstrating that price increases disproportionately affected widely used drugs. The proportion of NDCs from drugs supplied by 1 to 3 manufacturers that doubled in price within a year was 3.6 times higher in 2012 to 2015 than in 2005 to 2009 (4.6% vs 1.3%, respectively).

 Increases in generic drug prices are concerning because they affected widely used drugs and suggest that generic drug prices may be increasingly insensitive to competition.

Before 2010, changes in prices of generic drugs were higher for drugs supplied by a lower number of manufacturers. However, after 2010, price changes increased abruptly, and drugs supplied by 4 to 7 manufacturers showed increases similar to or even higher than those supplied by 1 to 3 manufacturers.

In 2013 and 2014, price changes averaged 29% and 24%, respectively, for drugs supplied by 1 to 3 manufacturers, 29% and 28% for 4 to 7 manufacturers, and 10% and 15% for more than 7 manufacturers.

Recent increases in generic drug prices are concerning because they affected multisource, widely used drugs. Rising generic drug prices may be due not only to insufficient market competition but also to the increasing insensitivity of prices to competition.

Several studies have quantified drug price inflation over the past decade, finding that although brand name and specialty drugs experienced the largest price increases, generic prices also increased over time.

 Rising generic drug prices have been attributed to the relatively low competition present in most generic markets—the average generic is only supplied by 2 manufacturers,

 and generic drug prices are not responsive to competition until at least 3 manufacturers have entered the market.

 However, prices of widely used generic drugs supplied by numerous manufacturers have recently experienced abrupt increases, in some cases triggering allegations of price fixing.

We quantified how 12-month changes in prices of all generic drugs available from 2005 to 2016 changed with the number of generic manufacturers supplying each drug, and we also evaluated the incidence of abrupt price increases across the study period. We hypothesized that price increases would be higher for generic drugs supplied by a lower number of manufacturers.

We obtained monthly wholesale acquisition costs (WACs) for all National Drug Codes (NDCs) available from 2005 to 2016 from AnalySource (reprinted with permission by First Databank, Inc) and annual counts of pharmacy claims from the University of Pittsburgh Medical Center (UPMC) Health Plan over the same time frame. WACs represent the list price from a manufacturer to a wholesaler or a direct purchaser and do not account for any type of manufacturer rebate or discount. An NDC is a unique and universal 10-digit number that identifies human drug products in the United States. UPMC Health Plan is a health insurer offering a variety of insurance products to more than 3.2 million members in Pennsylvania and parts of Ohio, West Virginia, and Maryland. Consequently, UPMC Health Plan offers a representative expression of medication use in an insured population. We standardized annual counts of pharmacy claims for each NDC, as if enrollment had remained constant since 2005.

Using data provided by First Databank, we identified all NDCs for generic drugs with WAC information available any time from 2005 to 2016. Generics entering the market within this time frame were included in the study sample starting the first year when their list price was available. The sample included 24,944 NDCs representing 1851 combinations of active ingredient and dosage form. Each year, NDCs were categorized according to the number of generic manufacturers—1 to 3, 4 to 7, and more than 7&mdash;offering each combination of active ingredient and dosage form in January. For example, flecainide tablets were supplied by 3 manufacturers in 2006 to 2008, 4 in 2009 to 2011, 6 in 2012 to 2014, 8 in 2015, and 9 in 2016. Consequently, NDCs for flecainide tablets were categorized under the 1 to 3 manufacturer group in 2006 to 2008, the 4 to 7 group in 2009 to 2014, and the more than 7 group in 2015 and 2016. We selected 3 as a cutoff because the FDA expedites review of generic drug applications in markets served by 3 or fewer manufacturers.

For every month from January 2006 to January 2017, we estimated the 12-month change in WAC for each NDC. For example, the 12-month change measured in January 2006 was calculated as the difference in WAC between January 2006 and January 2005, divided by the WAC in January 2005. This is the reason why, although we had 2005 to 2016 data, we report 12-month changes in WAC for 2006 to 2016. Because our results could be disproportionately affected by rarely used drugs with large price increases, we further calculated the average 12-month change after weighting each NDC by the count of pharmacy claims from UPMC Health Plan in the respective year. Finally, we calculated and reported the average of the 12-month change in WAC for every year of the study period.

 To assess the association between number of manufacturers and price changes while controlling for other factors that can affect price changes,

 we constructed a linear mixed model. The linear mixed model regressed 12-month change in WAC for each NDC against indicator variables for year, for groups defined by number of manufacturers, route of administration, therapeutic classes (list in the 

]), and interactions between the variables for number of manufacturers and for years. To account for the correlation in price changes between NDCs for a given product, we specified random intercepts for generic names. An autoregressive covariance structure was used to model the correlation between repeated observations for each NDC. We constructed a second version of this linear mixed model by weighting each NDC by the count of pharmacy claims from UPMC Health Plan in the respective year.

Finally, using the 12-month change in WAC measured in January of every year, we calculated the proportion of NDCs that had abrupt price increases, which we defined as increases greater than 20% within a year. We further quantified the proportion of NDCs that had even higher price increases (ie, greater than 50%, 100%, and 500%) within a year.

Before 2010, average 12-month price changes were consistently higher for drugs supplied by a lower number of manufacturers (

). Specifically, average 12-month price changes were at least 3 percentage points higher for drugs supplied by 1 to 3 manufacturers than those supplied by 4 to 7, and price changes were negative for those supplied by more than 7. For instance, in 2008, the average 12-month price change was 7% for drugs with 1 to 3 generic manufacturers, 4% for 4 to 7 manufacturers, and —8% for more than 7 manufacturers. However, after 2010, average 12-month price changes increased sharply for all groups, and drugs supplied by 4 to 7 manufacturers showed price increases similar to or even higher than those supplied by 1 to 3 (Figure 1). Drugs supplied by more than 7 manufacturers presented lower average price increases than those supplied by 7 or fewer, yet they exceeded 10% in 2013 and 2014. For instance, in 2013 and 2014, 12-month price changes averaged 29% and 24%, respectively, for drugs supplied by 1 to 3 manufacturers, 29% and 28% for those supplied by 4 to 7 manufacturers, and 10% and 15% for those supplied by more than 7. The magnitude of these estimates increased when we weighted NDCs by counts of pharmacy claims (Figure 1).

Results from mixed models were consistent with these observations (

): Average 12-month price changes in 2007 to 2010 did not significantly differ from those observed in 2006; however, price changes in 2011 to 2016 were significantly higher compared with 2006 (

 <.01). The effect of the number of manufacturers on price was different in 2012 and 2013 compared with 2006, but not in the remaining years (eAppendix Table). To facilitate interpretation of the results from these models, Figure 1 shows the price changes predicted by linear mixed models. In 2006 to 2011, predicted price changes were consistently higher for drugs supplied by a lower number of manufacturers; however, from 2012 to 2013, predicted price changes were similar for drugs supplied by 4 to 7 manufacturers and those supplied by 1 to 3 (Figure 1). The magnitude of the predicted estimates for price changes increased when each NDC was weighted by counts of pharmacy claims, and predicted price changes for drugs supplied by 4 to 7 manufacturers in 2012 to 2015 exceeded those for drugs supplied by 1 to 3 (Figure 1).

The proportion of NDCs experiencing abrupt price increases showed a similar trend (

). In 2005 to 2009 the proportion that experienced price increases of more than 20% within a year included around 4% of NDCs from drugs supplied by 1 to 3 manufacturers, 1.5% of those supplied by 4 to 7, and 0.4% of those supplied by more than 7. In this time period, an average of 1.3% of NDCs from drugs supplied by 1 to 3 manufacturers, 0.7% for 4 to 7, and 0.01% for more than 7 doubled in price within a year. After 2010, the proportion of NDCs experiencing abrupt price increases raised considerably: In 2012 to 2015, an average of 4.6% of NDCs from drugs supplied by 1 to 3 manufacturers, 3.4% for 4 to 7, and 0.15% for more than 7 doubled in price within a year. In other words, the proportion of NDCs that doubled in price within a year was 3.6 times higher in 2012 to 2015 than in 2005 to 2009 for drugs supplied by 1 to 3 manufacturers, and 5.1 times higher for those supplied by 4 to 7. NDCs from drugs supplied by 1 to 3 manufacturers experienced particularly abrupt increases in 2012: As many as 14.7% experienced a price increase of more than 20%, 7.4% doubled in price, and 2.0% quintupled. NDCs from drugs supplied by 4 to 7 manufacturers presented the strongest increases in 2014: 11.1% experienced a price increase of more than 20%, 5.0% doubled in price, and 2.2% quintupled. In 2016, the proportion of NDCs experiencing abrupt price increases decreased to levels similar to those observed in 2005 to 2009.

Before 2010, the increased competition associated with a greater number of manufacturers consistently resulted in lower price increases. However, in 2012 to 2015, price increases were similar or even higher for drugs supplied by 4 to 7 manufacturers compared with those supplied by 1 to 3, and were as high as 25%. In this time period, increases in prices of drugs supplied by more than 7 manufacturers averaged 10%. In addition, when NDCs were weighted by utilization, prices increased more dramatically, demonstrating that price increases disproportionately affected widely used drugs. The proportions of NDCs offered by 1 to 3 and by 4 to 7 manufacturers that doubled in price within a year were 3.7 and 5.1 times higher, respectively, in 2012 to 2015 than in 2005 to 2009.

Our findings add to the existing literature because they demonstrate that large increases in prices of off-patent drugs have not been limited to single-source drugs used in rare conditions,

 but have also affected commonly used multisource drugs. These price increases are particularly concerning because they can have a large impact on spending, as illustrated by our estimates that account for the relative utilization of drugs. With generic drugs supplied by 4 to 7 and more than 7 manufacturers experiencing average increases of 25% and 10%, respectively, in 2012 to 2015, our results suggest that rising prices of generic drugs may be due not only to limited competition in the generic market,

 but also to the increasing lack of sensitivity of generic drug prices to competition.

Although our study did not assess the reasons behind this increasing insensitivity of prices to competition, the high price increases observed in 2012 to 2015 could reflect the occurrence of several cases currently under investigation for alleged price collusion.

 Alternatively, they could be related to the so-called 2012 patent cliff, when several blockbuster brand name drugs—including atorvastatin, clopidogrel, olanzapine, quetiapine, escitalopram, montelukast, esomeprazole, and methylphenidate&mdash;lost patent protection.

 Specifically, manufacturers may have increased generic drug prices because they had fewer opportunities to generate revenue through 180-day exclusivity periods after the end of the patent cliff. Regardless of the underlying reasons, the sharp increase in the incidence of abrupt price increases indicates distorted pricing dynamics in the pharmaceutical market during this time period. However, we found that trends in price changes stabilized after 2015. The reason for price stabilization is also unclear, although it could be due to increased social and political pressure or the filing of several complaints alleging collusion in rising prices of generic drugs.

Future research should further evaluate whether the price trends observed in 2012 to 2015 for generic drugs are isolated and whether the price stabilization observed after 2015 is maintained over time. This will clarify whether competition in the generic market is enough to maintain low inflation rates or whether the implementation of policy solutions designed to cap inflation is needed.

First, in our analyses, we used WACs as pricing estimates, which do not account for manufacturer discounts or rebates. Nevertheless, list prices are often used to provide directional information about drug prices, and uninsured or underinsured patients are often exposed to these prices. Additionally, our focus on generic drugs also reduces the likelihood of the impact of rebates. Second, we used counts of pharmacy claims from UPMC Health Plan to weight each NDC by its relative utilization. The use of data from only 1 insurance carrier limits the generalizability of our results; however, the use of weights was necessary to confirm that the observed average increases in prices were not driven by disproportionate increases in prices of rarely used drugs.

We observed double-digit increases in prices of all generic drugs in 2012 to 2015, regardless of the number of manufacturers. Our results signal market failures in the generic drug market and warrant the consideration of policy strategies to increase the sensitivity of generic drug prices to competition.

 Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh (IH, MH), Pittsburgh, PA; Insurance Services Division, University of Pittsburgh Medical Center (CBG, NP, WHS), Pittsburgh, PA; Division of General Internal Medicine, School of Medicine, University of Pittsburgh (WFG), Pittsburgh, PA.

 The authors acknowledge funding from the Myers Family Foundation. Dr Hernandez is funded by the National Heart, Lung, and Blood Institute (grant number K01HL142847).

 Dr Parekh and Dr Good are employed by the University of Pittsburgh Medical Center (UPMC) Insurance Service Division, Center for Value-Based Pharmacy Initiatives. Dr Shrank is employed by UPMC Health Plan. 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. This study was presented at the International Society for Pharmacoeconomics and Outcomes Research Europe 2018 Annual Meeting in Barcelona, Spain, on November 13, 2018.

 Concept and design (IH, CBG, WFG, NP, MH, WHS); acquisition of data (IH, CBG, MH); analysis and interpretation of data (IH, CBG, WFG, NP, MH, WHS); drafting of the manuscript (IH, CBG); critical revision of the manuscript for important intellectual content (WFG, NP, MH, WHS); statistical analysis (IH); provision of patients or study materials (IH); obtaining funding (IH); and administrative, technical, or logistic support (IH).

 Inmaculada Hernandez, PharmD, PhD, Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, 637 Salk Hall, Pittsburgh, PA 15261. Email: inh3@pitt.edu.

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Articles

Regardless of the number of manufacturers, generic drug prices presented double-digit average increases from 2012 to 2015.

Number of Manufacturers and Generic Drug Pricing From 2005 to 2017

 To compare healthcare quality, utilization, and patient satisfaction between provider-led health plans (PLHPs) and non-PLHPs.

 Observational study of 2016 Medicare Advantage (MA) plans.

 We included 3 quality outcomes (MA Star Rating System, Healthcare Effectiveness Data and Information Set [HEDIS] effectiveness aggregate score, and HEDIS access aggregate score), 4 utilization outcomes (HEDIS average procedure rates, discharge rates, inpatient days, and readmission probability), and 1 patient satisfaction outcome (National Committee for Quality Assurance consumer satisfaction rating). We performed regression analysis to compare the 8 selected outcomes between PLHPs and non-PLHPs, controlling for key covariates, including region, profit status, patient risk, and patient-related and provider-related demographics.

 Our sample included 64 contracts offered by 31 PLHPs (representing 3,197,284 enrollees) and 311 contracts offered by 55 non-PLHPs (representing 13,881,210 enrollees). Compared with non-PLHPs, in our primary multivariable model, PLHPs were associated with higher star ratings (β = 0.41; 95% CI, 0.15-0.67), effectiveness scores (β = 3.11; 95% CI, 1.43-4.80), and patient satisfaction (β = 0.57; 95% CI, 0.30-0.84), and lower procedure rates (β = —0.47; 95% CI, –0.79 to –0.16). There were no significant differences in access, discharges, inpatient days, and readmission probability. The association between PLHPs and outcomes differed by plan size, nonprofit status, and region.

 Receipt of care within a PLHP was associated with improved quality, effectiveness, and patient satisfaction, as well as lower procedure rates. As providers bear increasing financial risk under alternative payment models, there is momentum to integrate healthcare provision and payment through PLHPs. Our results demonstrate the potential of such organizations to deliver high-quality care, although opportunities remain to optimize utilization.

In this cross-sectional study of 2016 Medicare Advantage plans, provider-led health plans (PLHPs) were associated with higher quality, effectiveness, and patient satisfaction and decreased procedure rates compared with non-PLHPs.

The association between PLHPs and outcomes differed by plan size, nonprofit status, and region.

There were no significant differences between PLHPs and non-PLHPs in access, number of inpatient discharges, duration of stay, and readmission probability.

As alternative payment models grow in popularity and momentum builds for providers to start their own health plans, our results demonstrate the potential of PLHPs to deliver higher-quality care and patient satisfaction, although opportunities remain to optimize utilization.

As healthcare providers accept increasing financial risk in alternative payment models, more provider organizations are expected to operate their own health insurance plans, known as provider-led health plans (PLHPs). Over the past 2 decades, PLHPs have become increasingly popular in the United States, with more than 100 plans covering more than 15 million individuals.

 Often referred to as vertical integration, the integration of healthcare providers and payers offers potential advantages to the patient, provider, and system. By inherently aligning payer—provider incentives and managing healthcare across a continuum of services, PLHPs may be particularly advantageous in population health management and, therefore, may have superior outcomes with lower premiums compared with non-PLHPs.

Our knowledge of the impact of PLHPs on outcomes remains limited and inconsistent.

 For instance, critics of PLHPs argue that they are not consistently associated with higher-quality healthcare and can lead to increased costs due to greater market power and administrative costs.

 Furthermore, it remains unknown how PLHP characteristics, including size, region, and nonprofit status, may affect outcomes. For example, nonprofit plans may perform better than for-profit plans,

 and larger plans may perform better than smaller plans through increased experience.

The objectives of this study were therefore to (1) determine the association between PLHP status and healthcare quality, utilization, and patient satisfaction and (2) determine whether these associations differed by plan size, nonprofit status, and region.

We conducted an observational study of Medicare Advantage (MA) contracts using December 2016 MA enrollment data from CMS. We focused on MA due to its large population and available outcome data that allow for standardized comparisons.

 We identified all MA contracts offered in 2016 with more than 20,000 enrollees to increase generalizability. For each contract, we obtained information on 3 quality outcomes, 4 utilization outcomes, and 1 patient satisfaction outcome. The quality outcomes were the 2017 MA Star Rating System (5-star maximum); 2016 Healthcare Effectiveness Data and Information Set (HEDIS) effectiveness aggregate score, defined as the average of 55 HEDIS Effectiveness of Care measures (100% maximum); and 2016 HEDIS access aggregate score, defined as the average of 2 HEDIS Access of Care measures (100% maximum). The utilization outcomes were 2016 HEDIS measures and included procedure rates, defined as average procedure rates per 1000 members for 13 selected procedures; discharge rates, defined as risk-adjusted discharges per 1000 members; inpatient days, defined as inpatient days per 1000 member-months; and risk-adjusted readmission probability. The patient satisfaction outcome was the 2016-2017 National Committee for Quality Assurance consumer satisfaction ratings, which are based on 2016-2017 Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys (5-point maximum) (see 

We categorized each MA contract as belonging to a PLHP or non-PLHP based on a publicly available list from the Robert Wood Johnson Foundation,

 which we enhanced to include additional PLHPs based on lists from McKinsey and Avalere (

 When there was uncertainty about PLHP status, we conducted an internet search to verify. We obtained region and profit status from the December 2016 MA enrollment list and patient risk from 2015 CMS plan payment data.

To compare how outcomes differed between PLHP and non-PLHP contracts, we constructed multivariable linear regression models using generalized estimating equations with exchangeable correlation matrices to account for correlation between contracts within health plans. For example, Aetna’s health plan offered 25 MA contracts in our data set. The model controlled for accessible covariates identified as meaningful from existing literature,

 including MA region, contract profit status, average MA patient risk score, and the following covariates, all of which were derived from Area Health Resources Files

 and weighted for county contract enrollment: percent urban residence, percent black/African American, mean per capita income, college education among population 25 years or older, percent poverty among population 65 years or older, population 65 years or older per 1000 population, hospital beds per 1000 population, and active physicians per 1000 population. Each contract was analytically weighted by enrollee number.

We conducted subgroup analyses to evaluate how the association between PLHP contracts and outcomes differed by PLHP size, profit status (for-profit vs nonprofit), and MA region. To assess outcome differences by size, we compared outcomes of the 6 PLHPs with at least 100,000 enrollees (Kaiser Permanente, UPMC, Healthfirst, Spectrum, Innovacare, and Tufts) with those of the remaining PLHPs. To assess PLHP effects stratified by region, we mapped our model results for each MA region, differentiating areas where PLHPs performed significantly better than non-PLHPs, worse than non-PLHPs, or where there was no difference. Subgroup analyses were based on the multivariable model above, except for regional analyses. Regional analyses only adjusted for profit status and patient risk score because the inclusion of additional covariates prevented the model from producing estimates for many regions. Finally, to explore whether our findings were driven by Kaiser Permanente, a notably high-quality plan, we ran our base-case models after excluding Kaiser Permanente contracts.

Analyses were performed using Stata 14 (StataCorp LP; College Station, Texas) and SAS 9.4 (SAS Institute Inc; Cary, North Carolina). Further information on data sources, variable definitions, and missing data is available in eAppendix Table 1 and 

Our study population included 64 contracts offered by 31 PLHPs (representing 3,197,284 enrollees) and 311 contracts offered by 55 non-PLHPs (representing 13,881,210 enrollees) (

). Unadjusted mean star ratings, effectiveness, access, and patient satisfaction were higher among PLHPs compared with non-PLHPs, whereas procedure rates, inpatient discharges, and inpatient days were lower.

In adjusted models, PLHPs were associated with higher star ratings (β = 0.41; 95% CI, 0.15-0.67), effectiveness (β = 3.11; 95% CI, 1.43-4.80), and patient satisfaction (β = 0.57; 95% CI, 0.30-0.84) compared with non-PLHPs. Procedure rates were lower for PLHPs than non-PLHPs (β = —0.47; 95% CI, –0.79 to –0.16). There were no significant differences in access, inpatient discharges, inpatient days, and readmission probability (Table 1).

 illustrates the results from subgroup analyses, which demonstrated that larger PLHPs had significantly higher star ratings (β = 0.57; 95% CI, 0.28-0.86), effectiveness (β = 4.63; 95% CI, 2.45-6.80), and patient satisfaction (β = 0.78; 95% CI, 0.54-1.02) compared with smaller PLHPs. After excluding Kaiser Permanente, PLHPs still had significantly higher star ratings (β = 0.22; 95% CI, 0.01-0.43), effectiveness (β = 1.22; 95% CI, 0.47-1.97), and patient satisfaction (β = 0.43; 95% CI, 0.12-0.73). Compared with nonprofit PLHPs, for-profit PLHPs had significantly lower star ratings (β = —1.07; 95% CI, –1.62 to –0.52), effectiveness (β = –5.59; 95% CI, –8.22 to –2.96), access (β = –7.66; 95% CI, –12.27 to –3.06), and patient satisfaction (β = –1.38; 95% CI, –2.70 to –0.06).

reflects the significance and direction of differences between PLHPs and non-PLHPs for each outcome by MA region. PLHPs performed significantly better than non-PLHPs in the following number of regions and outcomes, respectively: 6 of 16 regions for effectiveness, 5 of 16 for procedure frequency, 4 of 16 for inpatient days, 3 of 16 for star ratings and inpatient discharges, 3 of 12 for patient satisfaction, and 2 of 16 for access and readmission probability. PLHPs performed significantly worse than non-PLHPs in the following number of regions and outcomes, respectively: 2 of 16 for access, inpatient days, and readmission probability; 1 of 16 for selected procedure frequency; 1 of 12 for patient satisfaction; and 0 of 16 for star ratings, effectiveness, and inpatient discharges. Although regions in which PLHPs performed better than non-PLHPs generally varied by outcome, PLHPs performed consistently better in most outcomes in regions that included Texas, Illinois, and Wisconsin (

The results of our analyses show that MA contracts offered by PLHPs are associated with greater quality and patient satisfaction and decreased procedures. We further found that the effects of PLHP vary by size, nonprofit status, and region, with larger and nonprofit PLHPs performing better than their smaller and for-profit counterparts, respectively.

Our results on healthcare quality and patient satisfaction are consistent with findings by Johnson et al and Lyon et al, who found that PLHP MA contracts were associated with superior performance in quality and satisfaction measures.

 Prior literature on the impact of vertical integration on utilization has shown mixed results, with some studies’ results suggesting decreased utilization

 and others’ suggesting increased utilization.

 Although few studies have investigated access, Lyon et al demonstrated decreased provider access among PLHPs.

 Comparison with prior studies is nevertheless challenging, as some failed to differentiate between vertical and other forms of integration (eg, horizontal integration through provider consolidation), did not distinguish between separate outcome domains, and lacked standardized, timely outcomes. Our results are an important and distinctive contribution to existing literature because we evaluated the association between PLHPs and separate but standardized outcomes that reflect quality, access, utilization, and patient satisfaction.

PLHPs’ higher quality and satisfaction performance could be due to multiple factors. PLHPs may leverage the strengths and resources of insurers and providers to achieve common goals of delivering high-quality patient care. Potential resources include enhanced coordination between insurers and providers, use of unified electronic health records, integration of initiatives focused on high-value care, and streamlined interactions between patients and providers. Importantly, the populations served by PLHPs were lower risk than non-PLHPs and had nonsignificant trends toward being wealthier, more educated, and with fewer minorities. Although we adjusted for these differences in multivariable models, it is possible that we observed superior quality outcomes among PLHPs due to demographic-based differences.

Nevertheless, we observed no differences in access, inpatient days, discharges, or readmissions, thus identifying a need for PLHPs to streamline and optimize utilization.

We uniquely assessed how the effect of PLHPs differs with plan characteristics, identifying important differences in outcomes based on size, nonprofit status, and region. This suggests that not all PLHPs are alike, with the

heterogeneity potentially being caused by multiple factors. First, plans could have differences in organizational commitment to their populations. For example, commitment could be stronger in regions that emphasize population health and among larger plans that bear increased risk. Second, the complex relationship between plan enrollment and quality is important to consider: On one hand, larger plans seem more established as PLHPs and, therefore, may have more experience with the model, resulting in higher quality and efficiency; on the other hand, large plan size could result from the plan itself being high quality, as higher-quality plans tend to have higher enrollment.

 Third, although we adjusted for several demographic-based covariates, it is possible that the effect of PLHPs varies by their populations’ needs. For example, populations in Texas, Wisconsin, and Illinois may benefit more from vertical integration due to varying clinical needs and demographics. Fourth, our findings that nonprofit PHLPs performed better than for-profit PLHPs are consistent with previous literature

 and suggest that population health approaches differ by profit status, potentially due to differences in underlying incentives. Fifth, there is heterogeneity in the extent of integration employed by PLHPs. For example, some PLHPs restrict providers and enrollees within their own PLHP systems, whereas in others, providers and enrollees are permitted to see non-PLHP patients and be seen by non-PLHP providers, respectively. As a result, PLHPs with less mutual exclusivity in respective provider and payer markets may have less alignment of payer—provider incentives compared with more restrictive plans. Finally, PLHPs may take advantage of varying aspects of integration in their initiatives.

 For instance, some PLHPs might have a more unified electronic health record than others, whereas other PLHPs might have more collaborative payer—provider care management programs.

Although prior studies identified characteristics associated with health plan and accountable care organization success,

 future research could investigate factors and approaches associated with successful PLHPs.

Given that this is a cross-sectional study, we can make no inferences regarding causality. It is possible that high-performing providers practice in PLHPs or that health-seeking patients enroll in such plans. Second, some plans did not have data available for certain outcomes. However, the likelihood of missing data did not significantly vary between PLHPs and non-PLHPs and is therefore unlikely to bias results (eAppendix Table 3). Third, although we adjusted for differences between PLHPs and non-PLHPs, our findings may still be subject to residual confounding due to unobserved effects. Fourth, we did not adjust for market competition, which can impact pricing, networks, and population health approaches. Finally, our outcomes are not independent because star ratings include components of HEDIS and CAHPS in their calculations. Nevertheless, assessing PLHP effects on these outcomes is valuable because star ratings represent important composite measures, whereas HEDIS and CAHPS scores inform which domains may drive star ratings differences.

As alternative payment models grow, momentum is building to integrate provision and payment of care through PLHPs. Our analysis of 2016 MA plans demonstrates the potential of such organizations to deliver high-quality care, although opportunities remain in optimizing utilization.

 Division of General Internal Medicine (NP, TRR) and School of Pharmacy (IH), University of Pittsburgh, Pittsburgh, PA; Center for High-Value Health Care, UPMC Health Plan (NP, WHS), Pittsburgh, PA.

 The authors’ time was supported by the Health Resources and Services Administration National Research Services Award T32 for Primary Medical Care Research (T32NP22240) and by the National Center for Advancing Translational Sciences of the National Institutes of Health (KL2TR001856).

 Drs Parekh and Shrank are employed by UPMC. 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 (NP, IH, TRR, WHS); acquisition of data (NP); analysis and interpretation of data (NP, IH, WHS); drafting of the manuscript (NP, TRR); critical revision of the manuscript for important intellectual content (NP, IH, TRR, WHS); statistical analysis (NP, IH); and administrative, technical, or logistic support (WHS).

 Natasha Parekh, MD, MS, Division of General Internal Medicine, University of Pittsburgh, 200 Lothrop St, Pittsburgh, PA 15213. Email: nkp10@pitt.edu.

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The results of the study demonstrate the potential of provider-led health plans to deliver high-quality care and patient satisfaction. The relationships between these plans and outcomes differed by plan size, nonprofit status, and region.

Relationships Between Provider-Led Health Plans and Quality, Utilization, and Satisfaction

The rising prices of specialty drugs have prompted a debate about how medications are priced. With the average price of cancer drugs doubling in the last decade, the unsustainability of drug prices is especially concerning in oncology and hematology. The objective of this study was to compare the prices of monoclonal antibodies (mAbs) approved in the last 20 years by the FDA across disease states.

We identified all indications approved by the FDA for mAbs from 1997 to 2016 and calculated the annual price of 1-year treatment for each mAb—indication combination as the product of the US average wholesale price per milligram and the recommended dose.

We compared the annual price of treatment with each mAb across disease states using generalized linear models with gamma distribution and log link, controlling for route of administration, chemical structure, source, and time since FDA approval.

The average annual price of a mAb was $96,731, exceeding $100,000 for 34 mAb—indication combinations. Oncology and hematology mAbs represented 40% of the mAb–indication combinations approved, yet they accounted for more than 85% of those priced $100,000 or higher. After adjusting for factors that can affect production costs, the annual price of oncology or hematology mAbs was $149,622 higher than those used in cardiovascular or metabolic disorders; $98,981 higher than in immunology; $128,856 higher than in infectious diseases or allergy; and $106,830 higher than in ophthalmology (all 

The annual price of mAb therapies is about $100,000 higher in oncology and hematology than in other disease states.

The average price of a cancer drug has doubled in the last decade, and targeted therapies represent an important driver of that increase.

We identified all monoclonal antibody (mAb) therapies approved by the FDA in the last 20 years and compared their annual price across disease states.

Oncology and hematology mAbs represented 40% of the mAb—indication combinations approved, yet they accounted for more than 85% of those priced $100,000 or higher. With a median annual price of $142,833, the annual price of mAbs used in oncology or hematology was about $100,000 higher than those used in other disease states.

The unsustainable rising prices of specialty drugs have prompted a debate about how medications are priced. Manufacturers contend that high prices are essential to recoup research and development costs; however, a growing societal chorus believes that manufacturers are maximizing profits at the expense of patients and the healthcare system. Concerns about drug pricing policies are amplified in oncology and hematology,

 where vulnerable patients and their families often have unrealistic expectations about the value of treatment.

 Of note, the mean price of a cancer drug has doubled in the last decade, and targeted therapies represent an important driver of this increase.

 In this study, we compared the annual price of monoclonal antibody (mAb) therapies approved in the last 20 years by the FDA across disease states. Specifically, we evaluated whether the prices were higher for mAbs used in cancer than for those used in other disease states. We limited our analysis to mAbs to minimize the potential impact of varying production costs among different types of molecules.

We identified all indications approved by the FDA for mAbs from 1997 to 2016 using the FDA website.

 After excluding radioactive mAb&shy;—indication combinations (n = 1), antidotes (n = 1), and those approved for diagnostic purposes (n = 3), withdrawn from the market by 2016 (n = 2), or not available to the public for other reasons (n = 2), our sample included 107 unique mAb–indication combinations (

]). From the FDA-approved label of each mAb, we extracted the recommended dose for each indication, chemical structure (whole mAb, antigen-binding fragment antibody, or other), source (human, humanized, chimeric, or murine), and route of administration (subcutaneous, intravenous, intramuscular, or intraocular). We categorized indications into 5 disease states: oncology or hematology, cardiology or endocrinology, immunology, infectious diseases or allergy, and ophthalmology. We further classified oncology and hematology indications into 7 categories: bone cancer; breast cancer; gastrointestinal cancer; lung, head, or neck cancer; melanoma; hematologic malignancies and hematologic disorders; and other types of cancer, which included glioblastoma, metastatic renal cell carcinoma, urothelial carcinoma, and neuroblastoma (details in 

We extracted the US average wholesale price per milligram as of January 2017 from UpToDate

 and calculated the annual price of treatment for a standard patient—a 70-kg/1.80-m adult, a 40-kg patient in the case of mAb—indication combinations approved for juvenile conditions (ie, with childhood onset), or a 4.5-kg infant for mAb–indication combinations used in pediatrics (ie, for diseases that occur in the first weeks of life)&mdash;for each mAb–indication combination as the product of the recommended dose for 1 year of treatment and the price per milligram. We used lower-bound values for the weight of a standard patient because dosing is based on weight only for some mAbs, so using a higher value would increase the probability of type I error when comparing prices of mAbs that require dose adjustment with those that do not.

We constructed generalized linear models with gamma distribution and log link to evaluate how the annual price of treatment differed across disease states. We controlled for route of administration, chemical structure, time since FDA approval, and source, all of which can

 We followed the same methodology to examine how pricing of oncology and hematology mAb—indication combinations differed by type of cancer or hematologic disorder. All analyses were conducted at the mAb–indication combination level, meaning that for mAbs approved for more than 1 indication, each indication counted as a separate observation (details in 

Our sample included 107 mAb—indication combinations, with a mean (median) annual price of $96,731 ($58,968). The annual price of treatment exceeded $100,000 for 34 (32%) mAb–indication combinations and was highest for 2 indications of eculizumab ($800,280 for atypical hemolytic uremic syndrome and $592,654 for paroxysmal nocturnal hemoglobinuria) and lowest for denosumab, which is indicated for fracture prevention ($2465) (eAppendix Table 1). The annual price of treatment was highest for mAbs used in oncology or hematology (median, $142,833; interquartile range [IQR], $73,920-$164,291], followed by immunology (median, $53,969; IQR, $28,056-$68,770) (Figure and 

). Of 43 oncology and hematology mAb—indication combinations, 29 (67%) were priced higher than $100,000 per year of treatment. Although oncology and hematology mAb–indication combinations represented only 40% of all mAb–indication combinations approved in the last 20 years (43 of 107), they accounted for more than 85% of those priced $100,000 or higher (29 of 34). Within oncology and hematology indications, the annual price of treatment was highest for mAbs indicated for types of cancer delineated as “other,” which included glioblastoma, metastatic renal cell carcinoma, urothelial carcinoma, and neuroblastoma (median, $167,152; IQR, $158,456-$230,225), followed by lung, head, or neck cancer (median, $163,746; IQR, $162,086-$181,417).

After adjusting for route of administration, source, chemical structure, and time since FDA approval, the annual price of oncology or hematology mAbs was $149,622 higher than those used in cardiovascular or metabolic disorders; $98,981 higher than in immunology; $128,856 higher than in infectious diseases or allergy; and $106,830 higher than in ophthalmology (all 

<.001) (Table). Other than disease state, the chemical structure of a mAb was the only factor significantly associated with pricing. In subgroup analysis, we found no significant differences in prices of mAbs by type of cancer or hematologic disorder, which was probably due to the small sample size of each group, as well as the large price variability of hematology mAbs.

Our analysis is the first to compare the price of mAbs approved by the FDA in the last 20 years across disease states. Our results document the high prices of this type of medication; with an average price of $96,731, the annual price of treatment exceeded $100,000 for 32% of 107 mAb—indication combinations approved by the FDA between 1997 and 2016. Oncology and hematology mAbs represented 40% of the combinations approved, yet they accounted for more than 85% of those priced $100,000 or higher. After adjusting for factors that can affect production costs, we found that mAb therapies approved for the treatment of cancer and hematologic disorders are around $100,000 per year of treatment more expensive than mAbs used in other disease states.

Higher prices for mAbs used in oncology and hematology may be explained by multiple factors. First, in order to recover development costs, manufacturers set higher prices for drugs used for a short period of time or for drugs indicated in rare conditions. This likely explains why the 2 most expensive mAb—indication combinations were those with eculizumab, originally approved for paroxysmal nocturnal hemoglobinuria, which affects only 5000 patients in the United States.

 In addition, duration of treatment is usually shorter in cancer, where drugs are commonly used for weeks or months, than in other disease states where drugs may be used for years. Second, the therapeutic arsenal available for the treatment of some types of cancer and hematologic disorders is narrower than in other disease states, which makes patients and providers less responsive to the prices of these medications. Third, payers have limited levers to restrict access to, and hence lower prices for, cancer drugs.

 This is because Medicare Part D plans are required to include all cancer drugs in their formularies and some states also mandate the coverage of cancer drugs by private insurers.

This study was designed to describe patterns in pricing and did not aim to assess the process manufacturers apply when determining the asking price for their medications. Moreover, our study did not attempt to assess the value of the agents studied in terms of cost-effectiveness or clinical outcomes. In addition, our study did not evaluate the pricing of targeted therapies other than mAbs, and we used average wholesale prices, which do not account for manufacturers’ rebates. Nevertheless, this does not invalidate our results because the objective of our study was not to estimate net drug prices after discounts but to compare prices across disease states, and rebates should not differ across disease states.

Despite these limitations, our study has important implications. At a time when healthcare resources are constrained and threaten state and federal budgets, the rapidly rising costs of prescription drugs, specialty drugs in particular, will continue to garner media and policy attention.

 In the absence of some type of value framework where prices are justified by the value they bring to specific patients or the population, attention to drug pricing is likely to grow. There may be a unique opportunity for clinical experts and manufacturers to collaborate and redefine how the value of pharmaceuticals is measured and fundamentally shift the way manufacturers are reimbursed for high-cost medications like mAbs.

 Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh (IH, SWB, ASP, CGW, ARH, SS), Pittsburgh, PA; Insurance Division, University of Pittsburgh Medical Center (WHS), Pittsburgh, PA.

 University of Pittsburgh internal funds.

 Mr Bott owns stock in Merck, Incyte, and Johnson & Johnson. Mr Wolf is employed as an intern by Bristol-Myers Squibb. Dr Shrank is employed in the Insurance Division, University of Pittsburgh Medical Center, and has received consulting fees from Johnson & Johnson. 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 (IH, SWB, ASP, CGW, ARH, SS, WHS); acquisition of data (SWB, ASP, CGW, ARH, SS, WHS); analysis and interpretation of data (IH, SWB, ASP, ARH, WHS); drafting of the manuscript (IH, SWB, CGW, ARH, SS, WHS); critical revision of the manuscript for important intellectual content (ARH, WHS); statistical analysis (IH); obtaining funding (IH); and supervision (IH, WHS).

 Inmaculada Hernandez, PharmD, PhD, Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261. Email: inh3@pitt.edu.

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The annual price of monoclonal antibody therapies used in oncology and hematology is about $100,000 higher than those used in other disease states.

Inmaculada Hernandez, PharmD, PhD

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