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The American Journal of Managed Care May 2017
Drivers of Excess Costs of Opioid Abuse Among a Commercially Insured Population
Lauren M. Scarpati, PhD; Noam Y. Kirson, PhD; Miriam L. Zichlin, MPH; Zitong B. Jia, BA; Howard G. Birnbaum, PhD; and Jaren C. Howard, PharmD
Critical Incident Stress Debriefing After Adverse Patient Safety Events
Reema Harrison, PhD, MSc, BSc, and Albert Wu, MD, MPH
Assessing the Effect of the VHA PCMH Model on Utilization Patterns Among Veterans With PTSD
Ian Randall, PhD; Charles Maynard, PhD; Gary Chan, PhD; Beth Devine, PhD; and Chris Johnson, PhD
State Prescription Drug Monitoring Programs and Fatal Drug Overdoses
Young Hee Nam, PhD; Dennis G. Shea, PhD; Yunfeng Shi, PhD; and John R. Moran, PhD
Disparities in Diabetes and Hypertension Care for Individuals With Serious Mental Illness
Junqing Liu, PhD; Jonathan Brown, PhD; Suzanne Morton, MPH; D.E.B. Potter, MS; Lisa Patton, PhD; Milesh Patel, MS; Rita Lewis, MPH; and Sarah Hudson Scholle, DrPH
The Cost of Adherence Mismeasurement in Serious Mental Illness: A Claims-Based Analysis
Jason Shafrin, PhD; Felicia Forma, BSc; Ethan Scherer, PhD; Ainslie Hatch, PhD; Edward Vytlacil, PhD; and Darius Lakdawalla, PhD
Prescription Opioid Registry Protocol in an Integrated Health System
G. Thomas Ray, MBA; Amber L. Bahorik, PhD; Paul C. VanVeldhuisen, PhD; Constance M. Weisner, DrPH, MSW; Andrea L. Rubinstein, MD; and Cynthia I. Campbell, PhD, MPH
Opioid Prescribing for Chronic Pain in a Community-Based Healthcare System
Robert J. Romanelli, PhD; Laurence I. Ikeda, MD; Braden Lynch, PharmD; Terri Craig, PharmD; Joseph C. Cappelleri, PhD; Trevor Jukes, MS; and Denis Y. Ishisaka, PharmD
The Association of Mental Health Program Characteristics and Patient Satisfaction
Austin B. Frakt, PhD; Jodie Trafton, PhD; and Steven D. Pizer, PhD
Currently Reading
Medicaid Prior Authorization and Opioid Medication Abuse and Overdose
Gerald Cochran, PhD; Adam J. Gordon, MD, MPH; Walid F. Gellad, MD, MPH; Chung-Chou H. Chang, PhD; Wei-Hsuan Lo-Ciganic, PhD, MS, MSPharm; Carroline Lobo, MS; Evan Cole, PhD; Winfred Frazier, MD; Ping

Medicaid Prior Authorization and Opioid Medication Abuse and Overdose

Gerald Cochran, PhD; Adam J. Gordon, MD, MPH; Walid F. Gellad, MD, MPH; Chung-Chou H. Chang, PhD; Wei-Hsuan Lo-Ciganic, PhD, MS, MSPharm; Carroline Lobo, MS; Evan Cole, PhD; Winfred Frazier, MD; Ping
Enrollees in Medicaid plans employing prior authorization policies for opioid medications may have lower rates of opioid medication abuse and overdose.
ABSTRACT

Objectives:
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.

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

Methods: 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.

Results: 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; P <.001) and Low PA plans (ARR, 0.93; 95% CI, 0.87-1.00; P = .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; P = .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; P = .08).

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

Am J Manag Care. 2017;23(5):e164-e171
Takeaway Points

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.1,2 Perhaps the largest system-level investment in the United States to address abuse and prevent overdose has been prescription drug monitoring programs,3-5 which have shown mixed results for protecting patient health.3,6,7 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 medications8; this intervention has shown some promise for improving medication monitoring and reducing diversion.9 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.10

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.11 An extensive body of literature has shown cost-saving benefits of PA policies for a variety of medications, often expensive name brand drugs12,13; however, limited research has been conducted on their impact on patient-related opioid and quality-of-care outcomes.14-16 PA policies applied in public or commercial health insurance plans frequently result in reductions in medication use,17-20 but this result is often accomplished by placing administrative burdens on clinicians.21 

Medicaid programs serving low-income populations have federally allowable co-payments mandating that minimal out-of-pocket costs can be charged to enrollees.22 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.23 On average, Medicaid enrollees receive more than double the total annual opioid dose compared with the privately insured.24 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.14-16 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.

METHODS

Design


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,25 and statewide demographic profile (with the exception of lower rates of Hispanics)26 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.1,27 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 ajmc.com]). 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.28 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.

Variables

Outcomes. We identified opioid medication abuse following previously published approaches29,30 using International Classification of Diseases, 9th Edition (ICD-9) 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—not elsewhere classified])31 and had any overlapping fill for a opioid pain medication were categorized as having abuse (no abuse = 0, abuse = 1).29,30 Patients meeting this definition of abuse have been observed to have both a heightened overdose risk32 and serious behavioral, mental, and/or physical health problems.29,30 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.29,30

The opioid overdose indicator used in this analysis followed previously established methods for identifying prescription opioid overdose using ICD-9 codes within claims data.31 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.

PA indicator. 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.33 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. 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 Codes34,35).

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.36 We included in the model a modified Elixhauser Comorbidity Index, an indicator that used ICD-9 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.37 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.

Analyses

 
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