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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.
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; P <.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; P = .04) relative to No PA.

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; P = .02). There was a nonsignificant 12% reduction (95% CI = 0.76-1.02; P = .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% (P <.05) lower rates of abuse and 12% (P = .08) to 25% (P = .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.14,15  

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.38

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 conditions39 or transition to heroin use.2 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.21 


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,25 and demographics,26 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.46 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 opioids41,42 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,43 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 data47 and claims data do not account for cash payments to prescribers/pharmacies, which could influence observed associations were these data available.48


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