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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
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Ian Randall, PhD; Charles Maynard, PhD; Gary Chan, PhD; Beth Devine, PhD; and Chris Johnson, PhD
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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
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
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

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
This study investigates the impact of state prescription drug monitoring programs on drug overdose mortality rates for all drug categories.

Objectives: To examine the impact of prescription drug monitoring programs (PDMPs) on drug overdose deaths.

Study Design: We used variation in the timing of state PDMP legislation and implementation to estimate the impact of these programs on drug overdose mortality rates across all drug categories from 1999 to 2014 and separately for each category from 1999 to 2010. Data used include US all-jurisdiction mortality data, estimated population data, and sociodemographic data from the CDC and the US Census Bureau.

Methods: Multivariate regression models were applied to state panel data, including state and year fixed effects and state-specific linear time trends. Preprogram tests were used to assess the common trends assumption underlying our empirical approach. 

Results: The implementation of PDMPs was not associated with reductions in overall drug overdose or prescription opioid overdose mortality rates relative to expected rates in the absence of PDMPs. For most categories, PDMPs were associated with increased mortality rates, but the associations were statistically insignificant. In a subsample analysis of states with PDMPs in operation for 5 or more years, the programs were found to be associated with significantly higher mortality rates in legal narcotics, illicit drugs, and other and unspecified drugs.

Conclusions: PDMPs were not associated with reductions in drug overdose mortality rates and may be related to increased mortality from illicit drugs and other, unspecified drugs. More comprehensive and prevention-oriented approaches may be needed to effectively reduce drug overdose deaths and avoid fatal overdoses from other drugs used as substitutes for prescription opioids.
Am J Manag Care. 2017;23(5):297-303
Takeaway Points

Prior studies of state prescription drug monitoring programs (PDMPs) and fatal drug overdoses have either not examined specific drug categories or examined only selected specific drug categories, focusing on prescription opioids. This study examined the impact of PDMPs on drug overdose mortality rates across all drug categories from 1999 to 2014 and separately for each category from 1999 to 2010, including illicit drugs and other and unspecified drugs, and found that: 
  • PDMPs, on average, have not been effective in reducing drug overdose mortality rates. 
  • PDMPs may be associated with increased overdose mortality rates in drug categories other than prescription opioids.
Prescription drug overdoses have become one of the fastest growing and most serious public health concerns in the United States. The number of deaths has increased more than 7-fold: from about 6100 in 1980 to 47,055 in 2014,1-3 or approximately 129 deaths every day. In 2014, the overall US mortality rate was 823.7 per 100,000, and the drug overdose mortality rate was 14.7 per 100,000.1,2,4,5 In addition, recent reports indicate that fatal drug overdoses significantly contributed to the unexpected increase in mortality among midlife non-Hispanic whites.1,3,6 More than half of drug overdose deaths are caused by prescription drugs, and more than 70% of prescription drug overdose deaths are caused by opioid pain relievers.1,7,8

Inappropriate prescription drug use not only affects health outcomes, but is also correlated with increasing fraud, waste, and additional costs for taxpayers, employers, and insurers.9-12 For example, a study that examined medical and pharmacy claims data from 16 self-insured employer health plans reported that enrollees identified as having drug abuse or dependence had hospitalization rates 12 times higher than those without these conditions and annual healthcare and drug costs 8 and 5 times higher, respectively.9 The total societal costs of prescription opioid overdose, abuse, and dependence in the United States in 2013 was estimated at $78.5 billion, including healthcare costs, lost productivity, and criminal justice costs.10 

To address prescription drug misuse and abuse, an increasing number of states (currently more than 40) have implemented a prescription drug monitoring program (PDMP). These programs maintain statewide databases, collecting data on the prescribing, dispensing, and purchasing of controlled substances.13 These data can be used to identify suspected illegal activities, such as prescription diversions, doctor shopping, and pill mills; to inform public health initiatives; and to facilitate the treatment of drug addiction, among others. The Office of National Drug Control Policy (ONDCP) considers PDMPs an important tool to combat prescription drug overdose deaths. Implementing PDMPs was one of the 4 major areas in ONDCP’s 2011 Prescription Drug Abuse Prevention Plan, with the goals of having legislation in all 50 states establishing PDMPs within 36 months and decreasing by 15% the number of unintended opioid-related overdose deaths within 60 months.14 These goals were not met.

Despite the expansion of PDMPs, evidence on their effectiveness in reducing prescription drug abuse or misuse is inconclusive.15-22 For example, 1 study analyzed opioid abuse treatment admission data and found that PDMPs mitigated the increasing trend of opioid abuse and misuse.19 However, another study found no demonstrable decrease in prescription opioid abuse associated with PDMPs.20 Reviewing patients’ prescription history in a university medical center emergency department changed clinicians’ opioid prescription plans for 74 of 179 patients, with 61% receiving fewer opioids and 39% receiving more opioids than originally planned.21 A more recent study (2016) used the 2001 to 2010 data from the National Ambulatory Medical Care Survey and found that PDMPs were associated with a significant decrease in Schedule II opioids prescribing rates.22 

The existing literature on PDMPs and drug overdose deaths is limited. The results of a small number of earlier studies on this topic suggest that PDMPs might not be effective in reducing fatal drug overdoses, but the findings were not consistent.23-25 A more recent study using an interrupted time series design found that implementing a PDMP was associated with a decrease in prescription opioid-related overdose mortality rates.26 These studies focused on prescription opioid-related overdose deaths and/or overall drug overdose deaths. To our knowledge, no study has broken down drug overdose deaths across different classes of drugs, an important gap in the literature. Although PDMPs monitor only prescription controlled substances, they might also affect the use of other drugs, as individuals may switch to nonprescription drugs or find alternative ways of obtaining prescription medications.

Our study aimed to investigate whether PDMPs were effective in reducing fatal drug overdoses across all drug categories and separately for each category. Given the magnitude of the prescription drug epidemic and the expansion of PDMPs at the national level, evaluating PDMPs’ overall impact may be helpful for assessing the relative effectiveness of public policies designed to reduce drug overdoses. 


Study Design

We conducted our analysis in 2 parts. First, using publicly available mortality data from the Centers for Disease Control and Prevention (CDC),27 we examined overall drug overdose mortality rates in PDMP and non-PDMP states in the United States between 1999 and 2014. This database suppresses mortality data when the number of deaths is fewer than 10. Next, we applied the same analysis to the unsuppressed CDC mortality data obtained from the National Center for Health Statistics (NCHS) from 1999 to 2010—the maximal range available from the multiple-cause-of-death data at the time of our analysis—identifying drug overdose deaths by subcategory. 

In our study sample, 19 states began operating PDMPs sometime between 2002 and 2010: Alabama, Arizona, Colorado, Connecticut, Iowa, Louisiana, Maine, Minnesota, Mississippi, North Carolina, North Dakota, New Mexico, Ohio, South Carolina, Tennessee, Virginia, Vermont, West Virginia, and Wyoming. From 2011 to 2014, 15 more states implemented PDMPs: Alaska, Arkansas, Delaware, Florida, Georgia, Kansas, Maryland, Montana, Nebraska, New Hampshire, New Jersey, Oregon, South Dakota, Washington, and Wisconsin. Two states (“states” hereafter includes the District of Columbia) did not begin to operate PDMPs by the end of 2014: Missouri and the District of Columbia.28,29 The remaining 15 states were excluded as they implemented PDMPs before 2000 and did not have sufficient pre-implementation years available to support the empirical approach used in this paper. Figure 1 shows our study sample states. 

Drug overdose deaths were identified by International Classification of Diseases Codes 10th Revision (ICD-10 codes): X40-X44, X60-X64, X85, and Y10-Y14 for the underlying-cause-of-death data and T36.0-T50.9 for the multiple-cause-of-death data. A complete list of these ICD-10 codes is shown in eAppendix Table 1 [eAppendices available at]. 


The data used to measure drug overdose mortality rates include: 1) CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) database of mortality27; 2) de-identified individual-level unsuppressed mortality data for all jurisdictions of the United States, obtained from the NCHS; and 3) estimated population data produced by the US Census Bureau and NCHS, which were also extracted from the CDC WONDER website.27 The mortality data are based on the information from all death certificates filed in all jurisdictions in the United States. Each death certificate contains a single underlying cause of death and up to 20 multiple causes. Data used for covariates include population estimates from the US Census Bureau and NCHS27 and sociodemographic data from the Current Population Survey, produced by the US Census Bureau.30

Statistical Analysis 

To estimate the effect of PDMPs on fatal drug overdoses, we used multivariate regression models with state and year fixed effects and state-specific linear time trends. The inclusion of state fixed effects allows each state to serve as its own control group, eliminating all time-invariant unobserved differences across states. National and state trends in PDMP operation and drug overdose mortality rates, which might otherwise confound our estimates, were accounted for with year fixed effects and state-specific linear time trends.31 This approach contrasts with the analysis in Patrick et al, which relied on state fixed effects and a single national linear time trend.26 The exposure was defined as the state- and year-specific PDMP operation status (operated = 1, not operated = 0). The outcome variable was state-level, year-specific drug overdose mortality rates measured by the number of deaths per 100,000 individuals. To control for other differences across states, we included state-level, time-varying covariates that might be associated with drug overdose mortality rates and PDMP operation status; in particular, the percentages of a state’s population that is male, white, high school educated or better (age 25 or older), uninsured, and enrolled in the Medicaid program. We also controlled for median household income (in 2015 US$ for 1999-2014 and 2011 US$ for 1999-2010). Mortality rates were crude rates because the covariates we obtained were not age-adjusted.32 Clustered standard errors were used to correct for arbitrary patterns of serial correlation within states.

A key threat to establishing a cause-and-effect relationship between PDMPs and fatal drug overdoses is the possibility that states adopt PDMPs in response to changes in overdoses that depart from the state-specific linear time trends included in our models, or that adoption coincides with changes in these trends for other reasons. We tested for this possibility in an extended model presented in eAppendix Tables 2 and 3 by adding indicator variables for the year prior to enactment of the PDMP law and for the 2 years prior to enactment, labeled PRE1 and PRE2, respectively. If these preprogram indicators are small and statistically insignificant, it suggests that after adjusting for national and state trends, states adopting PDMPs would have experienced similar changes in fatal drug overdoses as the nonadopting states in the absence of a PDMP.33 

We also examined potential PDMP enactment effects for the period following the enactment of a PDMP law, but prior to the PDMP becoming operational, by including an indicator labeled PEPO (post enactment and pre-operation) in the extended model for these periods (years of post enactment and pre-operation = 1; the other years = 0). In addition, to examine potentially important differences in the PDMP effect based on program duration, we also conducted a subsample analysis of PDMPs operating for 5 or more years.

Statistical analysis was conducted using SAS version 9.4 (SAS Institute Inc; Cary, North Carolina) and Stata/IC version 14 (StataCorp LP; College Station, Texas). Institutional review board approval was not needed because no human participants were involved in this study. 


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