Walid F. Gellad, MD, MPH

 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

 The US opioid medication epidemic has resulted in serious health consequences for patients. Formulary management tools adopted by payers, specifically prior authorization (PA) policies, may lower the rates of opioid medication abuse and overdose. We compared rates of opioid abuse and overdose among enrollees in plans that varied in their use of PA from “High PA” (ie, required PA for 17 to 74 opioids), with “Low PA” (ie, required PA for 1 opioid), and “No PA” policies for opioid medications.

 Retrospective cohort study of patients initiating opioid treatment in Pennsylvania Medicaid from 2010 to 2012.

Generalized linear models with generalized estimating equations were employed to assess the relationships between the presence of PA policies and opioid medication abuse and overdose, as measured in Medicaid claims data, adjusting for demographics, comorbid health conditions, benzodiazepine/muscle relaxant use, and emergency department use.

The study cohort included 297,634 enrollees with a total of 382,828 opioid treatment episodes. Compared with plans with No PA, enrollees in High PA (adjusted rate ratio [ARR], 0.89; 95% confidence interval [CI], 0.85-0.93; 

<.001) and Low PA plans (ARR, 0.93; 95% CI, 0.87-1.00; 

= .04) had lower rates of abuse. Enrollees in the Low PA plan had a lower rate of overdose than those within plans with No PA (ARR, 0.75; 95% CI, 0.59-0.95; 

= .02). High PA plan enrollees were also less likely than No PA enrollees to experience an overdose, but this association was not statistically significant (ARR, 0.88; 95% CI, 0.76-1.02; 

 Enrollees within Medicaid plans that utilize PA policies appear to have lower rates of abuse and overdose following initiation of opioid medication treatment.

Health insurance payers can implement policies to help curb the opioid epidemic. This retrospective cohort study of Pennsylvania Medicaid data examined the associations between Medicaid plans that utilized prior authorization (PA) policies for opioid medications and enrollees developing opioid medication abuse or experiencing overdose. 

Enrollees within plans that subjected opioid medications to PA policies had lower rates of opioid medication abuse and overdose after initiating opioid medication treatment. 

Future research should work to extend these findings in order to support systematic and large-scale implementation of PA policies for opioid medications.

The US opioid medication epidemic has had serious effects on public health, including opioid-related overdoses and mortality.

 Perhaps the largest system-level investment in the United States to address abuse and prevent overdose has been prescription drug monitoring programs,

 which have shown mixed results for protecting patient health.

 Another system-level intervention is a lock-in program, wherein patients who exceed filling pattern thresholds are limited to specific providers/pharmacies to receive future opioid medications

; this intervention has shown some promise for improving medication monitoring and reducing diversion.

 Formulary management tools may represent a valuable set of interventions that payers can employ to control opioid medication consumption, deter shopping behaviors, and improve quality and safety.

One formulary management tool that may be used to address the opioid crisis is prior authorization (PA), a requirement placed on some medications by payers to verify that the medication is necessary and/or patients meet the medical criteria for use.

 An extensive body of literature has shown cost-saving benefits of PA policies for a variety of medications, often expensive name brand drugs

; however, limited research has been conducted on their impact on patient-related opioid and quality-of-care outcomes.

 PA policies applied in public or commercial health insurance plans frequently result in reductions in medication use,

 but this result is often accomplished by placing administrative burdens on clinicians.

Medicaid programs serving low-income populations have federally allowable co-payments mandating that minimal out-of-pocket costs can be charged to enrollees.

 Therefore, these programs are particularly reliant on PA policies, as opposed to other formulary management tools that use cost sharing to influence demand. Research has shown that approximately one-fourth of Medicaid patients who regularly use opioid medications (>90 days) are engaged in problematic opioid consumption behaviors.

 On average, Medicaid enrollees receive more than double the total annual opioid dose compared with the privately insured.

 To date, 19 states’ Medicaid programs have required PA for long-acting opioids, and study results show these policies can reduce long-acting opioid fills.

 The extent to which PA policies can help reduce the problematic opioid-related outcomes of abuse and drug overdose is unknown. We hypothesized that enrollees within Medicaid fee-for service (FFS) programs and managed care plans employing PA policies for opioid medications would have lower rates of abuse and opioid medication overdose compared with patients enrolled in Medicaid plans without PA. Understanding the potential associations between PA and abuse and overdose may provide health systems and payers with an additional tool to address problematic opioid-related outcomes.

This investigation was a retrospective cohort study that utilized Pennsylvania Medicaid data from 2010 to 2012. The Pennsylvania Medicaid program is among the largest in the United States in both expenditures and enrollment; the state's healthcare utilization, access,

 and statewide demographic profile (with the exception of lower rates of Hispanics)

 are similar to those seen across the nation. Pennsylvania has the eighth highest overdose rate in the country, and opioid prescribing rates are consistently above national averages.

 We obtained Pennsylvania Medicaid data directly from the Pennsylvania Department of Human Services (PADHS) for all FFS and managed care enrollees.

We used Medicaid enrollment data and pharmacy/medical claims to establish an analytic cohort of Medicaid enrollees who initiated a new opioid medication not used for addiction treatment (eAppendix [eAppendices available at 

]). We included patients in the study cohort who were aged 18 to 64 years, not dually eligible for Medicare (given that we could not capture medication use for those >64 years and dually eligible), without previous cancer treatment, not in long-term care for 90 or more days, and not receiving hospice services (as opioid use patterns would likely differ for these groups). We identified the index opioid exposure event as patients’ first oral, transdermal, or submucosal opioid medication fill.

To identify new episodes of opioid medication treatment, we excluded individuals from the cohort that possessed a record of filling any opioid medication, had an opioid use disorder, or experienced an opioid medication overdose in the 6 months prior to the index opioid fill. This step in the cohort construction allowed us to create a “clean” baseline period for patients before they were exposed to opioid medications and potentially developed abuse or experienced an overdose. Lapses in fills greater than 6 months following the index fill ended patients’ eligible treatment episodes. We selected a 6-month gap in fills to end the episode to be consistent with prior studies validating this approach in behavioral health populations.

 We examined numbers of patient episodes by plan PA status, and no major differences were detected (results not shown). This study was designated exempt by the University of Pittsburgh Institutional Review Board.

 We identified opioid medication abuse following previously published approaches

International Classification of Diseases, 9th Edition

) coding classifications and pharmacy claims. After the index fill, enrollees who had any code for an opioid use disorder (304.0, 304.00, 304.01, 304.02, 304.03, 304.7, 304.70, 304.71, 304.72, 304.73, 305.5, 305.50, 305.51, 305.52, 305.53) or opioid medication poisoning (965.00 [opium poisoning], 965.02 [methadone poisoning], 965.09 [opiate poisoning—not elsewhere classified], E.850.1 [accidental methadone poisoning], and E.850.2 [accidental opioid poisoning&mdash;not elsewhere classified])

 and had any overlapping fill for a opioid pain medication were categorized as having abuse (no abuse = 0, abuse = 1).

 Patients meeting this definition of abuse have been observed to have both a heightened overdose risk

 and serious behavioral, mental, and/or physical health problems.

 We recognize this definition of abuse does not match the 

Diagnostic and Statistical Manual for Mental Disorders 

definition, but we chose to employ this term given its previous use in the literature.

The opioid overdose indicator used in this analysis followed previously established methods for identifying prescription opioid overdose using 

 The overdose indicator occurred after the index opioid fill, comprised opioid medication poisoning codes (965.00, 965.02, 965.09, E.850.1, E.850.2), and was dichotomized (no overdose = 0, overdose = 1). These codes capture nonfatal and fatal overdose events resulting in hospitalization, emergency department (ED) visits, and/or other medical care. We did not capture overdose events outside of the healthcare system, which may have largely been untreated within Pennsylvania given the limited and variable availability of naloxone to public safety and prehospital healthcare professionals during the study years. We acknowledge that abuse and overdose are both constructed using poisoning claims and that there is some overlap between these measures; however, we chose this approach (ie, not removing the poisoning codes from the abuse indicator) to remain consistent with the previous literature.

 Specification of plans in Pennsylvania Medicaid with PA took place in partnership with the Bureau of Managed Care within PADHS. Officials from PADHS provided FFS PA information and contacted all managed care plans (N = 8) via e-mail requesting historical formulary medication management policy information between January 1, 2010, and December 31, 2012. Qualitative responses were transferred into a data-tracking template. Given the variation in use of PA across plans in our study data, we followed an ordinal classification approach for categorization of policies similar to those previously employed in the literature.

 One insurance plan was labeled “Low PA” (ie, required PA for 1 opioid); 2 plans were labeled “High PA” (ie, required PA for 17-74 opioids); and 6 plans were labeled “No PA” based on the number of generic, brand name, and combination product medications subjected to PA (Table 1). PA policies were active before or on the first day of our study observation period (January 1, 2010), thus limiting our ability to compare the differences among plans across time. We therefore conducted a cross-sectional comparison of enrollees across plan types.

 Covariates were measured in the enrollees’ baseline periods. Demographic covariates included in the model were age (18-29, 30-39, 40-49, 50-64 years), sex, race/ethnicity (white, black, Hispanic, other), Medicaid eligibility category (General Assistance, Supplemental Security Income, Temporary Assistance for Needy Families), Medicaid plan type (FFS, managed care organization), and urban/rural county of residence (coded using Rural-Urban Continuum Codes

We likewise included measures of comorbidity in the models, which were also measured at baseline. Specific comorbidities included: alcohol use disorders (abuse/dependence), nonopioid drug use disorders (abuse/dependence [eg, cocaine, marijuana] not including Not Elsewhere Classified codes, which clinicians may have used in lieu of opioid use disorder codes), several indicators for mental health disorders (adjustment, anxiety, mood, personality, miscellaneous), separate indicators for pain diagnoses (back, neck, arthritis/joint, headache/migraine), and HIV/AIDS.

 We included in the model a modified Elixhauser Comorbidity Index, an indicator that used 

 codes to measure patient comorbidity within administrative claims data from hospitals and physician services. This indicator was modified by removing comorbidities described above that we included as individual covariates. ED use was also included in the model (≥1 visit = 1, <1 visit = 0).

We included morphine milligram equivalents (MME) following the index fill but before the occurrence of abuse or overdose. MME was constructed by converting the total within-episode opioid supply into morphine equivalents, dividing by the days' supply, and coding into 4 levels: ≥100 MME/day, 50 to <100 MME/day, 20 to <50 MME/day, <20 MME/day.

 Indicators of medication use that are known correlates with abuse/overdose were also added as covariates in the model, which included any use of benzodiazepines and muscle relaxants in the baseline period. All covariates were categorical with the exception of the Elixhauser Comorbidity Index, which was a count indicator, and age, which was ordinal.

With the exception of descriptive demographic characteristics that were calculated at the person level for patients enrolled in the 3 PA plan types, analyses for this study were conducted at the episode level. Our modeling strategy needed to account for 2 features of our data: heterogeneity in the duration of opioid use across episodes and some enrollees having multiple episodes. The importance of accounting for episode-level events for individual enrollees is based on the dynamic nature of patient behaviors and health status across time, which can alter an individual’s risk. We therefore employed generalized linear models with generalized estimating equations (GEE) using log link function and Poisson distribution where follow-up length (day) was treated as offset in the model, and the exchangeable covariance structure was employed to account for standard error correlation.

These models were able to account for greater exposure to opioids and PA policies within an episode and greater numbers of episodes, and they were applied to examine the association between the outcome variables of opioid medication: 1) abuse and 2) overdose and the predictor variable of PA adjusted for all covariates described above. We also report abuse and overdose rates with 95% confidence intervals (CIs) by PA type adjusted for all covariates and offset log length of episode. All analyses were conducted with SAS version 9.4 (SAS Institute; Cary, North Carolina).

In an alternative model specification, we estimated both the abuse and overdose outcome analyses using a propensity score matching approach wherein we matched individuals in the High and Low PA plans to those in No PA plans. Results showed no substantive differences; therefore, we chose to present the adjusted GEE results instead of the matched sample for the purpose of simplicity and to maximize the sample size included in our analyses.

The analytic cohort included 297,634 individual plan enrollees with a total of 382,828 opioid treatment episodes. Many enrollees within the cohort had multiple opioid treatment episodes, with patients having an average of 2 episodes (median = 1; results not shown). Table 2 presents descriptive patient-level demographic and episode-level health and medication use characteristics. The largest proportions of patients were aged 18 to 29 years (n = 140,876; 47.3%) and were female (n = 212,209; 71.3%). The largest proportional differences among PA plans were in race and rural/urban living location. White enrollees were most prominent in High PA plans (77.2%; n = 79,965), and Black (47.9%; n = 15,950) and Hispanic (31.2%; n = 10,386) enrollees were most prominent in the Low PA plan. Most Low (99.9%; n = 33,226) and No PA (95.1%; n = 152,913) enrollees lived in urban locations compared with 63.7% (n = 65,948) of High PA plan enrollees.

The most common level of opioid consumption within episodes (60.9%-68.6%) was 20 to 49.9 MME per day following the index opioid fill. The unadjusted rate of abuse within episodes was 3.46 for High PA plans, 2.36 for the Low PA plan, and 3.39 for the No PA plans after the index opioid medication fill. The unadjusted rate of overdose in episodes was 0.26 in High PA plans, 0.19 for the Low PA plan, and 0.29 in No PA plans after the index fill (Table 3).

Table 4 shows the results of the GEE analyses adjusted for demographic and health status differences across plan types, which demonstrated that individuals in High PA plans were 11% less likely to develop opioid medication abuse after their index opioid medication fill compared with those within plans with No PA (95% CI = 0.85-0.93; 

<.001). Enrollment in the Low PA plan was also associated with a 7% lower rate of developing opioid medication abuse after the opioid medication index fill (95% CI, 0.87-1.00; 

In terms of the relationship between PA and overdose, enrollment in the Low PA plan was associated with a 25% lower rate of experiencing an overdose following the index opioid medication fill (95% CI = 0.59-0.95; 

 = .02). There was a nonsignificant 12% reduction (95% CI = 0.76-1.02; 

= .08) in overdose for enrollees in High PA plans. We recognize that the High PA plans had the highest unadjusted rate of abuse, but after adjustment, the No PA plan had the highest. To identify which set of covariates influenced this change, we re-estimated our model by adding blocks of variables to the model in a stepwise fashion (eg, block 1 = abuse, overdose, and MME; block 2 = demographics; block 3 = mental/behavioral health and co-occurring health conditions [ie, pain, Elixhauser Index]). Results showed that adding the demographic block resulted in the change. We also re-estimated the GEE analyses without the MME covariate to examine its influence on model outcomes. The magnitude and direction of all effects were unchanged after removing MME.

Table 5 reports adjusted rates based on the GEE analyses for abuse and overdose per person-days (where 452.1 [standard deviation = 299.2] was the average number of per-person follow-up days for subjects in the cohort). The adjusted rates of abuse were 2.49% for High PA plans, 2.58% for the Low PA plan, and 2.76% for No PA plans per average person-days. The adjusted rates of overdose were 0.21% for High PA plans, 0.17% for the Low PA plan, and 0.23% for No PA plans per average person-days.

The opioid medication epidemic has brought to the forefront of healthcare practice and policy the need to identify and intervene with patients engaged in abuse and at risk for overdose. Policy-level efforts that limit access to opioid medications and influence patient and prescriber behaviors have the potential to make an important impact on reducing negative patient outcomes. We analyzed data from Medicaid enrollees who developed opioid pain medication abuse or experienced an overdose after initiating opioid treatment who were within plans that utilized PA policies compared with plans that did not. Our findings showed a minority of plans implemented PA policies (3 of 9 plans) and there was substantial variation in the number of medications within plans subjected to PA policies (range = 1-74).

Enrollment in High and Low PA plans was associated with modestly lower adjusted rates of opioid medication abuse, and enrollment in the Low PA plan was associated with lower adjusted rates of overdose. These results are consistent with those of previously published studies that have examined the effects of PA on opioid medication fills. Specifically, our findings that PA was associated with 7% to 11% (

= .02) lower rates of overdose are consistent with studies that have reported 8% to 19% reductions in long-acting opioid medication fills among enrollees in plans that utilized PA policies.

A central point of importance for our findings is that they advance previous studies reporting reductions in long-acting opioid medication fills, which is an outcome metric with limited ability to differentiate between patients with problematic use and those who may benefit from opioid medications. Assessing only fills as an outcome also cannot disentangle patient and prescriber behaviors. Accordingly, evaluating the benefits of PA policies by changes in abuse and overdose more effectively discriminates reductions in potential repercussions of opioid use—perhaps demonstrating an outcome especially relevant to combatting the national opioid epidemic. Further, reductions in abuse, for example, could have valuable ramifications for health systems, payers, and prescriber stakeholders as patients with opioid use disorders have higher healthcare needs, utilization, and costs.

Future research should seek to extend our findings by examining the effect of PA within an analytical framework capable of examining both within- and between-group differences over time. Such studies should seek to account for legitimate pain management needs of patients. In particular, although abuse and overdose are both important outcomes for patient safety and health, future studies should examine potentially unintended consequences of PA plans on patients, such as undertreatment of painful conditions

 If future research continues to provide support for PA, broader implementation of these policies may necessitate streamlined and automated approaches to minimize the disruption to the medical/pharmacy workflow.

These findings should be viewed in light of certain limitations. First, while we recognize that the strengths of our study include possessing actual PA information from Medicaid plans, having complete FFS and managed care data, and Pennsylvania being similar to other programs in the nation with respect to healthcare utilization, access,

 it nonetheless represents 1 state in the United States.

Second, the last year for our data was 2012, and some analyses have shown reductions in opioid abuse and diversion in more recent years.

 Furthermore, the larger Medicaid landscape has evolved since this date, including the expansion of Medicaid through the Affordable Care Act in Pennsylvania and many other states.  Studies conducted with more recent data may yield different estimates as a result of these changes. PADHS also recently implemented additional restrictions on opioids

 that may yield even greater benefit. However, these policies went into effect after our study period ended so we were unable to evaluate them.

Third, we used a simple and straightforward approach to categorizing PA schemes (High, Low, and No based on the number of products subject to PA). It is possible our data collection method did not capture other aspects of these policies, such as the ease of use that may influence prescribing behavior. It is also possible that we did not capture information on all plan features or policies that may influence opioid prescribing and use. In light of this, we recognize our characterization of the policies may not capture the full range of interventions plans may have had in place. We note, however, that enrollees with evidence of opioid medication misuse have an equal possibility of enrollment in the state Medicaid agency-operated lock-in program.

Fourth, whereas the abuse measure is one of the more common and valid indicators in the field,

 it has the potential to misclassify individuals engaged in legitimate use of opioids. Moreover, while we have been able to adjust for a number of patient-level characteristics in our analyses that could have introduced bias into our findings, other individual-level factors and regional variations in our outcomes could have influenced study outcomes. Future research should seek to employ quasi-experimental designs with comparison groups, such as difference-in-differences analyses, to help better understand the impact of PA.

Last, we recognize opioid use disorders are likely undercoded within claims data

 and claims data do not account for cash payments to prescribers/pharmacies, which could influence observed associations were these data available.

This study examined associations between PA requirements and developing abuse or experiencing an opioid medication overdose following an index opioid medication fill for Medicaid enrollees. These findings extend previous research by demonstrating improved outcomes among patients within PA plans in terms of lower rates of abuse and overdose. Future research should seek to extend these findings within more rigorous causal designs/analyses and among other Medicaid and commercial payer data to continue building evidence for PA policies reducing problematic opioid behaviors and consequences. 

 School of Social Work (GC), and School of Medicine (C-CHC), Department of Psychiatry (GC), and School of Medicine, Division of General Internal Medicine (AJG, WFG, WF), and Center for Pharmaceutical Policy and Prescribing (GC, AJG, WFG, JMD), and Graduate School of Public Health (C-CHC, CL, EC, PZ, JMD), University of Pittsburgh, Pittsburgh, PA; VA Pittsburgh Healthcare System (AJG, WFG), Pittsburgh, PA; University of Arizona, College of Pharmacy (WL-C), Tucson, AZ; Pennsylvania Department of Human Services, Medical Assistance Programs (DK), PA.

 Centers for Disease Control and Prevention (U01CE002496).

 Dr Gordon has received grants from NIH, VA, and other public grants; the contents do not represent views of the US department of Veterans Affairs or the US government. 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 (GC, AJG, WFG, WF, DK, JMD); acquisition of data (GC, PZ, JMD); analysis and interpretation of data (GC, AJG, WFG, WL-C, DK, CL, EC, WF, PZ, JMD); drafting of the manuscript (GC, AJG, C-CHC, WL-C, CL, JMD); critical revision of the manuscript for important intellectual content (GC, AJG, WFG, WL-C, CL, EC, WF, DK, JMD); statistical analysis (GC, C-CHC, WL-C, PZ); obtaining funding (GC, JMD); administrative, technical, or logistic support (EC, JMD); and supervision (JMD).

 Gerald Cochran, PhD, University of Pittsburgh, 4200 Forbes Ave, 2117 CL, Pittsburgh, PA 15260. E-mail: gcochran@pitt.edu.&ensp;

1. Rudd RA, Aleshire N, Zibbell JE, Gladden RM. Increases in drug and opioid overdose deaths—United States, 2000-2014. 

. 2016;64(50-51):1378-1382. doi: 10.15585/mmwr.mm6450a3.

2. Compton WM, Jones CM, Baldwin GT. Relationship between nonmedical prescription-opioid use and heroin use. 

. 2016;374(2):154-163. doi: 10.1056/NEJMra1508490.

3. Clark T, Eadie J, Knue P, Kreiner P, Strickler G; The Prescription Drug Monitoring Program Center of Excellence; Heller School for Social Policy and Management, Brandeis University. Prescription drug monitoring programs: an assessment of the evidence for best practices. The PEW Charitable Trusts website. http://www.pewtrusts.org/~/media/assets/0001/pdmp_update_1312013.pdf. Published September 20, 2012. Accessed May 2017.

4. As the prescribing practices of emergency providers come under enhanced scrutiny, watch for red flags of drug-seeking behavior. 

5. Paulozzi LJ, Kilbourne EM, Desai HA. Prescription drug monitoring programs and death rates from drug overdose. 

. 2011;12(5):747-754. doi: 10.1111/j.1526-4637.2011.01062.x.

Enrollees in Medicaid plans employing prior authorization policies for opioid medications may have lower rates of opioid medication abuse and overdose.

Medicaid Prior Authorization and Opioid Medication Abuse and Overdose

Long-acting insulin analogues (eg, insulin glargine and insulin detemir) are an alternative to neutral protamine Hage-dorn (NPH) insulin for maintaining glycemic control in patients with diabetes. Clinical trials comparing analogue insulin and NPH have neither been adequately powered nor had sufficient follow-up to examine long-term health outcomes.

 To compare the effects of NPH and long-acting insulin analogues on long-term outcomes.

This retrospective observational study relied on administrative data from the Veterans Health Administration and Medicare from 2000 to 2010. Local variations in analogue insulin prescribing rates were used in instrumental variable models to control for confounding. Outcomes were assessed using survival models.

The study population included US veterans dually enrolled in Medicare who received at least 1 prescription for oral diabetes medication and then initiated long-acting insulin be-tween 2001 and 2009. Outcomes included ambulatory care—sensi-tive condition (ACSC) hospitalizations and mortality.

: There was no significant relationship between type of insulin and ACSC hospitalization or mortality. The hazard ratio for mortality of individuals starting a long-acting analogue insulin was 0.97 (95% CI, 0.85-1.11), and was 1.05 (95% CI, 0.95-1.16) for ACSC hospitalization. Differences in risk remained insignificant when predicting diabetes-specific ACSC hospitalizations, but starting on long-acting analogue insulin significantly increased the risk of a cardiovascular-specific ACSC hospitalization.

 We found no consistent difference in long-term health outcomes when comparing use of long-acting insulin analogues and NPH insulin. The higher cost of analogue insulin without demonstrable clinical benefit raises questions of its cost-effectiveness in the treatment of patients with diabetes.

The progressive nature of type 2 diabetes mellitus requires many patients to initi-ate insulin. This research compares the effects of the neutral protamine Hagedorn (NPH) and analogue insulin on long-term outcomes. n Analogue insulin is significantly more expensive than NPH.

Clinical trials comparing analogue insulin with NPH have not been adequately powered nor had sufficient follow-up to examine long-term health outcomes.

There were no significant differences in the risk of ambulatory care—sensitive condition hospitalization or mortality for patients who initiated analogue insulin compared with NPH.

The higher cost of analogue insulin without demonstrable clinical benefit raises questions of its cost-effectiveness in the treatment of patients with diabetes.

The progressive nature of type 2 diabetes mellitus requires many patients to use insulin to maintain glycemic control.

 Neutral protamine Hagedorn (NPH) insulin was the most commonly used intermediate- to long-acting insulin until the introduction of the long-act-ing insulin analogues: insulin glargine in 20002,

 The long-acting insulin analogues were designed with properties that prolonged absorption, and with a flattened activity peak that extended duration of ef-fect and also resulted in a reduced risk of hypoglycemia.

	The significantly higher cost of analogue insulin compared with NPH

 led researchers and policy makers to compare the efficacy and cost of these medications. Several reviews found no significant difference between analogue insulin and NPH on glycemic control or severe hypoglycemia (ie, low glucose level requiring assistance from another person) but did show reduced likelihood of nocturnal hypoglycemia for patients using analogue insulin.

	Despite these short-term differences, potential long-term benefits have been difficult to test due to the short time frame of studies comparing analogue insulin with NPH.2

Some cost-effectiveness studies were based on modeling that used clinical trial results as predictors of long-term treatment ef-fects (eg, Center for Outcomes Research Diabetes Model).1 Other cost-effectiveness studies used retrospective claims that may better reflect clinical settings outside of trials. Results from these studies are mixed, with some concluding analogue insulin was cost-effective

 and others concluding analogue insulin was not an efficient use of healthcare resources.

 However, an important limitation of these claims-based stud-ies was the assumption that no systematic, unobserved differ-ences existed between the groups being compared (eg, those that started on NPH compared with analogue insulin).

	To bridge this gap in the evidence, we used national Vet-erans Health Administration (VA) records to systematically compare long-term outcomes for patients using NPH or ana-logue insulin. We used a novel approach to an established observational comparative effectiveness method, instru-mental variables, to obtain unbiased estimates despite the possibility of unobserved differences between groups.

Patient-level national data from the VA were used and supplemented with data from Medicare to ensure com-pleteness in the measures of outcomes, as VA patients often use non-VA facilities for hospital care.

The study was reviewed and approved by the Institutional Review Board at the VA Boston Healthcare System.

All prescription claims for metformin, sulfonylurea, thiazolidinedione, and long-acting insulin (ie, NPH, insu-lin glargine and insulin detemir, and mixtures of short- and long-acting insulins) between 2000 and 2007 were extracted from VA pharmacy files (Figure). Eligible indi-viduals had a history of being prescribed at least 1 oral diabetes medication, and initiated long-acting insulin between February 1, 2001, and December 31, 2009. The initiation date for the long-acting insulin was the start of the study period (“index date”) for each patient and the prior 12 months was the baseline period. To ensure that all hospitalizations were recorded, we further limited the cohort to include only those enrolled in Medicare as well as the VA. The last index date permitted was at the end of 2009, to allow a minimum 12-month outcome period. This resulted in a cohort of 142,940 patients, including 118,878 who initiated NPH and 24,062 who initiated ana-logue insulin.

The main objective of the study was to compare the long-term effects of NPH and analogue insulin. Patients who initiated NPH or an NPH/regular in-sulin mixture were compared with patients who started glargine, detemir, or short-act-ing and long-acting mixtures that included analogue insulin. To improve comparabil-ity among patients, we attempted to isolate patients at a similar point in the progres-sion of diabetes. Consequently, patients were required to have a prescription for an oral diabetes medication during the base-line period before initiating insulin. Eighty percent of the individuals remained on the same insulin they started (NPH or analogue insulin). Among those who started on analogue insulin, 99% re-ceived glargine.

Provider Practice Pattern as Instrumental Variable

The principal threat to a simple comparison of out-comes is that the selection of treatment by patients and providers could be influenced by unmeasured differences in patient risk (selection bias or confounding by indica-tion). To address this, we developed instrumental vari-ables models, which identify a factor (the instrumental variable [IV]) that influences treatment but is effectively random with respect to patient risk and other potential confounders.

The statistical model isolates the com-ponent of treatment variation attributable to the IV and measures the relationship between this component and outcomes.

 The success of the approach depends on the effective randomness of the IV (controlling for all the other variables in the model) as well as the strength of the IV’s influence on treatment status.

	VA patients are assigned to their primary care physi-cian by variable and often arbitrary methods, so in this study, the IV was the proportion of long-acting insulin prescriptions written for analogue insulin by each pro-vider during an individual’s baseline period19—the 12 months before the index date on which analogue insulin was prescribed. The instrument assigned to each individ-ual was the proportion of analogue insulin prescriptions written by the provider who prescribed the initial insulin prescription on the index date.

For example, if an individual started on long-acting in-sulin on January 1, 2003, their baseline period was January 1, 2002, to December 31, 2002 (see 

). We identified the provider that prescribed insulin on Jan-uary 1, 2003, and calculated their proportion of analogue insulin prescriptions in that baseline period (January to December 2002). An individual’s insulin prescription does not contribute to their instrumental variable calcu-lation, eliminating concerns about selection bias. Provid-ers and patients were aligned based on the index date to minimize confounding that could occur if patients later switched providers.

	If a provider prescribed insulin to less than 10 unique patients during the baseline period (53% of the time), the rate at the community-based outpatient clinic (CBOC) or VA medical center (VAMC) at which the provider prac-ticed was used.

The IV will not be effective if correlated with other provider characteristics that might also affect outcomes, causing biased estimates of the treatment-outcome rela-tionship. Therefore, we included 3 provider-level process quality variables: percent of glycated hemoglobin (A1C) labs ≥9%,

 percent of blood pressure readings ≥140/90 mm Hg,

 and percent of low-density lipoprotein choles-terol labs >100 mg/dL.22 These variables were computed at the same provider, CBOC, or VAMC level, and time periods as the IV prescribing rate.

Additional control variables computed at baseline included patient age, sex, race, A1C, serum creatinine, urine microalbumin, body mass index (see Table 1 for cat-egorizations), 29 indicator variables for comorbidities,23 8 indicator variables for the components of the Young dia-betes severity index,24 and indicator variables for calen-dar years corresponding to index dates. Individuals can be diagnosed with multiple comorbidities (eg, obesity and congestive heart failure [CHF]).

Outcomes included mortality and hospital admission (VA or Medicare) for any of 13 ambulatory care—sensitive conditions (ACSCs) as defined by the Agency for Health-care Research and Quality.

These hospitalizations are hypothesized to be preventable with high-quality outpa-tient care and include several diabetes and cardiovascular complications such as uncontrolled diabetes, short- and long-term complications of diabetes, CHF, and chronic obstructive pulmonary disease. Sensitivity analyses es-timated models for specific ACSC hospitalization types most closely related to diabetes and cardiovascular dis-ease. The VA Vital Status File, which determines the date of death from VA, Medicare, and Social Security Admin-istration data, was used to determine mortality.

There were no significant differences in the risk of ambulatory care—sensitive condition hospitalization or mortality between patients who initiated analogue insulin compared with the neutral protamine Hagedorn. 

Walid F. Gellad, MD, MPH

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