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Effectiveness of Prescription Monitoring Programs in Reducing Opioid Prescribing, Dispensing, and Use Outcomes: A Systematic Review
ARTICLE IN PRESS The Journal of Pain, Vol 00, No 00 (), 2019: pp 1−11 Available online at www.jpain.org and www.sciencedirect.com
Review Article Effectiveness of Prescription Monitoring Programs in Reducing Opioid Prescribing, Dispensing, and Use Outcomes: A Systematic Review Maria N. Wilson, Jill A. Hayden, Emily Rhodes, Alysia Robinson, and Mark Asbridge Department of Community Health & Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
Abstract: Prescription monitoring programs (PMPs) house and monitor data about the prescribing practices of health care providers, as well as medications received by patients. PMPs aim to promote the appropriate use of prescription opioids by providing this information to prescribers and dispensers. Our objective in this systematic review was to comprehensively identify and assess the available evidence about the impact of PMPs on opioid prescribing and dispensing, multiple provider use for obtaining opioids, inappropriate opioid prescribing, and the extent of nonmedical prescription opioid use. We used a comprehensive search strategy and included study designs that could determine changes in outcomes with the implementation of a PMP. We included 24 studies; 75% of studies were conducted in the United States, and studies encompassed data years from 1993 to 2014. Overall, we did not find evidence to support an association between PMPs and decreased opioid prescribing and dispensing. We found limited, but inconsistent, evidence that PMPs were associated with reduced schedule II opioid prescribing and dispensing, as well as multiple provider use. Covariate adjustment was often inadequate in analyses, as was the timing of outcome and PMP measurement. Future studies should broaden their geographic scope to other countries and use more recent data with standard measurement. Perspective: This systematic review aimed to determine the effectiveness of PMPs in changing prescribing practices and prescription opioid use. The findings from this review will inform policymakers and PMP administrators about the current state of the evidence on program effectiveness. © 2019 by the American Pain Society Key Words: Prescription monitoring programs, opioids, prescription drugs, drug diversion, evaluation. orth America is amid an opioid crisis, which has its roots in opioid prescribing for noncancer pain.37 Each year, 1 in 7 Canadians take a prescribed opioid on the advice of their physician,59 a rate second only to the United States in highest prescribing worldwide.36 In the United States, 2.4 million people meet the criteria for severe opioid use disorder involving dependence on opioid analgesic medications, heroin, or both.37 These high levels of opioid use have led to many problems, including an increase in reported opioid misuse,7 illicit opioid use,35 overdoses and death,4,19 drug diversion, and crime,4,5,23 along with a
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substantial economic impact.3,30,69 Both Canada and the United States have reported record high levels of opioid overdoses in recent years.48,63 Most jurisdictions in North America have prescription monitoring programs (PMPs).25,28,51,52,64 PMPs are one of several initiatives aimed at promoting the safe and appropriate use of monitored prescription medications.25,52,64 PMPs house data and monitor the prescribing and dispensing practices of health care providers such as physicians and dentists, as well as medications received by patients. They aim to ensure that controlled substances like opioids are prescribed in appropriate
Received January 8, 2019; Revised March 15, 2019; Accepted April 21, 2019. This research was funded by the Canadian Institutes of Health Research Operating Grant: Opioid Crisis Knowledge Synthesis/Subvention (Grant #397982). The authors have no conflicts of interest to declare. Supplementary data accompanying this article are available online at www.jpain.org and www.sciencedirect.com.
Address reprint requests to: Mark Asbridge, Centre for Clinical Research, 5790 University Avenue, R99m 407, Halifax, NS B3H 1V7, Canada. E-mail: [email protected] 1526-5900/$36.00 © 2019 by the American Pain Society https://doi.org/10.1016/j.jpain.2019.04.007
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quantities, following best practice guidelines, are not coprescribed with potentially harmful substances if avoidable (eg, benzodiazepines in the case of opioids8) and are only provided to patients when safe and necessary.52 In so doing, PMPs help to reduce overall exposure to opioids in the population, lower the risk of diversion of supply (the illegal transfer of drugs from a person receiving the prescription to another person), and decrease the number of individuals at risk for subsequent dependence and harm.57 PMPs differ on several characteristics between jurisdictions where they have been implemented, including which substances are monitored and whether health care providers are required to query PMP data before prescribing or dispensing controlled substances. Actively checking patient profiles may allow providers to make a more informed decision on whether to prescribe to a patient based on their prescription history.32 PMPs may also be used to monitor health care provider prescribing practices to note when irregularities in prescribing occur. This information is used, in turn, by PMP managers and regulatory bodies to inform providers of potentially inappropriate prescribing, leading to sanctioning, mentoring, education, and/or training to reduce further occurrences.10 Several direct and indirect benefits of PMPs are described in the literature by health care providers, including the ability to monitor patients’ use history,29,38 improving patient safety,32 increasing comfort levels with prescribing,39 assisting in the recognition of drug-seeking behaviors,38,39 facilitating discussions with patients,29,38,58,62 informing prescription decisions,29,68 and helping providers to moderate the overall amount of opioids they prescribe to patients and within the community.39,58 However, PMPs are not without their criticisms. Barriers to PMP use described in the literature include the administrative burden of checking PMP data55, not seeing the value in PMP information34, and privacy concerns.34 Furthermore, PMPs have been criticized for not addressing the root causes of addiction, and there are concerns that they may hinder the ability to treat pain or drive consumers to black market supply or to more dangerous street opioids such as heroin.12,16 To date, no systematic review has synthesized the evidence on PMP effectiveness in changing outcomes related to opioid prescribing. A review of this nature is warranted given the significant resources that are dedicated to PMPs on an on-going basis: with estimated startup costs of a PMP in the United States ranging between $450,000 and $1.5 million, along with an average annual cost of $500,000 for maintenance.42 This study aims to determine the impact of PMPs on a) opioid prescribing and dispensing, b) multiple provider use for obtaining opioids, c) potentially inappropriate opioid prescribing, and d) nonmedical prescription opioid use.
Methods We used a standard systematic review approach33 and followed the PRISMA reporting guidelines.53
Data Sources and Search Strategy We conducted a comprehensive literature search using several data sources to retrieve all relevant publications. The electronic search strategy was developed using an iterative process in consultation with a medical librarian and was designed to be wide in scope (see Appendix A for database search strategies). We used EndNote X8.1 software (ResearchSoftware.com, Amsterdam, the Netherlands) to de-duplicate search results and manage citations.13 Our search strategy included the following: 1. Electronic searches of the literature in the following databases with the help of an experienced medical librarian searched up to and including January 22, 2018: Medline, Embase, CINAHL, PsycInfo, Web of Science, and Dissertation and Theses Databases. 2. Contacting of authors of key papers. 3. Review of personal libraries of the research team. 4. Search of the grey literature including CADTH, Health Canada, CIHI, CMA Infobase, and a targeted Google search. 5. A manual search of the reference lists of all included studies, related systematic reviews, and all additional relevant reviews identified in the electronic search.
Study Selection We included full published peer-reviewed reports of study data, from any countries, in all languages. Study designs were restricted to comparative studies that could determine changes in outcomes with the implementation of a PMP: pre−post studies, controlled before/after, case control, interrupted time series, or cluster randomized, controlled trial designs.
Population of Interest We included evidence from any jurisdiction (municipal, provincial/state, national) or institutional level (clinic, hospital, system) that had implemented a PMP.
Intervention of Interest We considered the intervention of interest the presence of a PMP, defined as a program that specifically monitors the outpatient prescription dispensing of opioids (or other drugs) by health care providers. PMPs vary considerably in legal structure, the presence and nature of sanctions and penalties, linkages to local governing medical bodies, and the types of data collected on prescriptions, prescribers, and patients. To ensure the broad scope of our review, we included all types of PMPs. To be considered for inclusion, the jurisdiction being studied had to have an operational PMP (not pending or forthcoming). Comparison groups were jurisdictions that had not implemented a PMP. Historical comparisons were included (ie, pre-PMP implementation and post-PMP implementation in the same jurisdiction[s]).
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Outcomes of Interest Across Domains We included 4 main outcomes of interest related to the effectiveness of PMPs compared with no PMPs (historical or comparison jurisdiction[s]): a. Change in the overall volume of opioids prescribed or dispensed or the proportion of specific opioids (eg, oxycodone, hydrocodone) prescribed or dispensed. We included studies that measured per capita, by provider, or by patient profile (defined daily dose per patient or number of patients prescribed). b. Change in rates of multiple provider use, defined as obtaining prescriptions from multiple health care providers in a window of time deemed by the study as inappropriate, either identified with administrative data or self-reported. c. Change in rates of inappropriate prescribing or dispensing practices (ie, not following guidelines for dose or quantity, as defined by the study). d. Change in rates of nonmedical prescription opioid use, either identified with administrative data or self-reported.
Risk of Bias and Assessment of Quality Two reviewers assessed the risk of bias in studies providing evidence about PMP effectiveness using the Quality in Prognostic Studies tool.31 The Quality in Prognostic Studies tool, designed to assess prognostic factor studies, assesses 6 relevant domains of potential bias: study participation, study attrition, prognostic factor measurement (ie, PMP exposure), outcome measurement, study confounding, and statistical analyses and reporting.
Data Extraction For all included studies, data extraction was completed by 2 independent reviewers using pretested data extraction forms developed in Covidence.67 Any discrepancies in data extraction (including risk of bias assessments) were discussed and the assessment of a third reviewer was sought for resolution when necessary. We extracted relevant study details (authors, year, jurisdiction, study design, sample size, response rate) and population characteristics (pharmacists, physicians, dentists), data sources (focus group, survey, administrative) outcomes (including any bivariate and multivariate associations between the outcome and the presence of a PMP), and all variables controlled for.
The Journal of Pain 3 equivalents (MME) and the number of prescriptions in PMP versus non-PMP jurisdictions in this synthesis. In the case of overlapping data across included studies, we present the study results with the most data years available in the primary narrative synthesis. We used Excel 201646 for data management and Stata 1565 for descriptive analyses and calculating pooled estimates.
Results The study selection process is outlined in Fig 1. After screening 2,185 citations at the title level and 968 at the abstract level, as well as conducting a citation search and a search of the grey literature, we screened 161 citations at the full text level. Overall, reviewers were discordant on 6.6% of studies at the title or abstract level, and all conflicts were discussed and resolved. We included a total of 24 studies in the present review related to the impact of PMPs on 1 of our 4 outcomes of interest (Table 1). The most common reason for study exclusion was no or inappropriate comparison jurisdiction (35.8%; Fig 1). These studies either had no comparison jurisdiction (ie, descriptive study of opioid-related outcomes in a PMP jurisdiction only), or the comparison jurisdiction included a PMP (ie, comparing the effect of mandatory vs nonmandatory PMP checking). Eighteen of these studies took place in the United States (75.0%), 5 in Canada (20.8%), and 1 in France (4.2%). The included studies were published between 2004 and 2018, encompassing data years from 1993 to 2014. Five studies (20.8%) reported on outcomes from surveys, and the remaining studies used administrative data (eg, insurance claims). The study population varied from the general population, to insured populations receiving prescriptions, to physicians prescribing opioids (Table 1). Detailed results on associations reported by each study and outcome are found in Appendix C.
Risk of Bias The risk of bias for all included studies is summarized in Table 2 (detailed summary by study in Appendix B). The proportion of studies with low risk of bias by domain were study participation (70.8%), study attrition (91.7%), study confounding (37.5%), PMP exposure and measurement (54.2%), outcome measurement (54.2%), and statistical analysis and reporting (70.8%). Only 1 study was rated as having a low risk of bias on all 6 domains (4.2%), whereas 8 studies had ≥1 domains rated as having a high risk of bias(33.3%).
Data Synthesis Owing to heterogeneity in the presentation of outcomes, we conducted a narrative synthesis of each outcome, considering the strength and consistency of results. For prescribing and dispensing outcomes, we synthesized outcomes on any grouping of opioids monitored (or “specific” opioids) reported in ≥3 publications. In the case of multiple outcome measures available for a study, we extracted all outcomes; however, priority was given to measures that considered morphine milligram
Prescribing and Dispensing Outcomes Results for all prescribing and dispensing outcomes are summarized in Table 3.
Opioid Prescribing and Dispensing Overall Eighteen studies examined the association between PMP status and opioid prescriptions or dispensing (including
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Records idenfied through database searching (n = 3675) Duplicate arcles excluded (n = 1490) Records screened at tle level (n = 2185) Records excluded (n = 1217) Records screened at abstract level (n = 968) Records excluded (n = 817) Records added from citaon search (n = 8) Records added from grey literature search (n = 2)
Full-text arcles assessed for eligibility (n = 161)
Studies included in qualitave synthesis (n = 24)
Full-text arcles excluded, with reasons (n = 137) No/Inappropriate comparison jurisdicon: 49 PMP not present/not part of intervenon: 37 Duplicate publicaon: 12 No primary study data: 10 Inappropriate outcome: 22 Not a full text publicaon: 7
Figure 1. PRISMA diagram for study screening and inclusion process.
shipments).2,6,11,14,18,21,22,27,40,41,43,45,47,49,57,60,61,71 Thirteen of these studies examined overall opioids (ie, not a specific drug or schedule of opioids) prescribed or dispensed, with 3 focusing only on a number of states where a PMP was implemented during the study period, 3 focusing on specific jurisdictions (including 2 from Canada), and 7 focusing on PMP compared with non-PMP states or provinces. Data from these studies examined overall opioids prescribed or dispensed from 5 unique sources, and 9 unique time periods: National Ambulatory Medical Care Survey (NAMCS) 2001 to 2010 and 2012, Automation of Reports and Consolidated Orders System (ARCOS) 2000 to 2013, Medicaid 1999 to 2011, IMS Health 2008 (United States), and 2010 to 2012 (United States, Florida only) and 2005 to 2010 (Canada) and 1997 to 2001 (Canada, Newfoundland only), and the CAMH Monitor 2010 to 2011 (Table 1). Seven studies found no association with opioid prescription outcomes and the presence of a PMP, overall2,21,22,27,40,43 or over time.2,45 Three studies found decreases in opioids prescribed or dispensed in PMP jurisdictions compared with those without PMPs. One study45 observed that opioid prescriptions decreased more rapidly over time in states with a required
use PMP. Another reported a statistically significant decrease in the number of opioids dispensed in the jurisdiction from 1997 to 2001; however, they noted a slight increase in prescriptions from 2000 (the year a PMP was implemented) to 2001, although no measure of statistical significance was presented.18 The third study reported a greater decrease in the number of patients with opioid prescriptions as well as the MME of prescriptions over time among high-risk prescribers (top 5th percentile of opioid prescribers) in Florida compared with Georgia (a non-PMP state at the time).11 No change in these outcomes among low-risk prescribers was observed. Among other included studies that were not part of the primary narrative synthesis, 3 found no statistically significant association between PMP status and opioids prescribed.6,49,71 Brady6 conducted a sensitivity analysis looking at each included state individually, finding a statistically significant decrease in MMEs dispensed per capita in 9 states, no change in 14 states, and a significant increase in 8 states. One study found a decrease in the mean MME of opioids dispensed per month in PMP states versus non-PMP states, but no significant change in the number of prescriptions per month.47
Characteristics of 24 Included Studies on PMP Effectiveness in Changing Prescribing and Dispensing Outcomes STUDY POPULATION
OUTCOME GROUPS
Ali et al1
United States
General population
Bao et al2,* Brady6 Chang et al11,y Curtis et al14 Dormuth et al17,y
United States United States United States United States Canada
Doyle18,y
Canada
Fischer et al22 Fischer et al21,y
Canada Canada
Pain patients in ambulatory care General population State residents who received prescriptions Insured population Provincial residents receiving social assistance or senior citizens, and who visited a pharmacy General population (prescriptions), physicians (potentially inappropriate prescribing) General population General population
Gomes et al26,y Goodin27
Canada United States
Insured population Insured population
Lin et al40,y
United States
Mallatt41,z
United States
McDonald et al43 Meara et al44
United States United States
Ambulatory care patients with noncancer chronic pain General population (ARCOS), insured population (Medicaid) general population Insured population
Meinhofer45 Moyo et al47,*
United States United States
General population Insured population
Prescriptions/dispensing Prescriptions/dispensing
Paulozzi et al49 Pradel et al20,y
United States France
General population Insured population
Prescriptions/dispensing Multiple provider use
Radakrishnan54,* Reisman et al57,y Simeone and Holland60 Simoni-Wastila and Qian61 Yarbrough71,*
United States United States United States United States
General population General population General population Insured population
Nonmedical prescription opioid use Prescriptions/dispensing Prescriptions/dispensing Prescriptions/dispensing
United States
Insured population
Prescriptions/dispensing
Multiple provider use, nonmedical prescription opioid use Prescriptions/dispensing Prescriptions/dispensing Prescriptions/dispensing Prescriptions/dispensing Multiple provider use
Prescriptions/dispensing, potentially inappropriate prescribing Prescriptions/dispensing Prescriptions/dispensing, nonmedical prescription opioid use Multiple provider use Prescriptions/dispensing prescriptions/dispensing Prescriptions/dispensing prescriptions/dispensing Potentially inappropriate prescribing, multiple provider use
Abbreviation: NSDUH, National Survey on Drug Use and Health. *Study only includes states with PMPs implemented during the study period. yStudy only focuses on some PMP states or jurisdictions within a country by choice, owing to analytic constrictions, or owing to availability of data; zARCOS data uses all states, Medicaid uses a subset.
OUTCOME DATA SOURCE, YEARS
UNIT OF ANALYSIS
NSDUH, 2004−2014
State year
NAMCS, 2001−2010 DEA ARCOS, 1999−2008 IMS Health LifeLink LRx database, 2010−2012 AdvancePCS database, 2000 BC Ministry of Health data warehouse, 1993−1997
State year State quarter State month County year Province month
IMS Health Canada database, 1997−2001
Province year
IMS CompuScript database, 2005−2010 Centre for Addiction and Mental Health (CAMH) Monitor (CM); 2010−2011 Ontario Public Drug Benefit Database, 2007−2013 Medicaid Drug Rebate Program data, 1999−2011; i3 InVision Data Mart database; 2007−2009 NAMCS, 2012
Province year Province year
Medicaid State Drug Utilization Data and ARCOS, 2000 −2014 IMS Health LRx database, 2008 Medicare Provider Analysis and Review (MEDPAR), Outpatient, Carrier, and Medicare Beneficiary Summary files, 2006−2012 ARCOS, 2000−2013 Medicare Part D Prescription Drug Event claims, 2008−2012 ARCOS, 1999−2005 General Healthcare Fund reimbursement database, 2000, 2002, 2004, 2005 NSDUH, 2004−2011 ARCOS, 1997−2003 ARCOS, 1997 and 2003 MarketScan COB administrative claims data, 2007 Medicare Part D Prescription Drug Event claims, 2010−2013
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COUNTRY
Province month State year State year State quarter county year State year
State quarter State month State year Regional semester (6 months) State year State year State year State year State year
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STUDY ID
Wilson et al
Table 1.
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Table 2.
Risk of Bias Summary for 24 Included Studies by Quality in Prognostic Studies Domain
DOMAIN RATING Low Moderate High
STUDY PARTICIPATION
STUDY ATTRITION
STUDY CONFOUNDING
PMP MEASUREMENT
OUTCOME MEASUREMENT
STATISTICAL ANALYSIS AND REPORTING
17 7 0
22 2 0
9 7 8
13 11 0
13 10 1
17 3 4
Prescribing and Dispensing of Specific Opioids
units of schedule II opioid MMEs dispensed27 and 2) mean MME of schedule II opioids dispensed per month in PMP states versus non-PMP states.47
Schedule II opioids, schedule III opioids, oxycodone, and hydrocodone were included as drugs of interest for prescribing or dispending outcomes in ≥3 studies. Other drug groupings were not narratively synthesized, as they were present in <3 studies, including buprenorphine,27 methadone,27,60 schedule IV and/or V opioids,47,71 and morphine.57,60
Schedule III Opioids Four studies examined the association between PMPs and schedule III opioid prescribing or dispensing, using 2 unique data sources. Three studies found no statistically significant association, 2 of which used Medicaid data.2,27,71 One study found a slight decrease in the mean MME of schedule III opioids dispensed per month in PMP states versus non-PMP states, also using Medicaid data.47 Studies examining schedule III opioids were rated as having either a low or moderate risk of bias on all domains.
Schedule II Opioids Five studies reported on the association of schedule II opioid prescribing or dispensing and PMP status,2,14,27,47,71 with data from 3 unique sources. All studies adjusted for covariates or accounted for potential confounding by using propensity score matching. Studies examining schedule II opioids were rated as having either a low or moderate risk for bias on all domains. One study found a greater decrease in visits with ≥1 schedule II opioid prescribed over time in PMP states compared with non-PMP states using NAMCS data.2 Another also found a statistically significant decrease in prescription claim rates for schedule II opioids per 1,000 total prescription claims in PMP counties using data from the AdvancePCS database.14 The study covering the most years of ARCOS data found no change in the amount of schedule II opioids prescribed based on PMP status.71 The 2 other studies reported statistically significant decreases in the 1) total Table 3.
Oxycodone and Hydrocodone Five studies examined oxycodone prescriptions and dispensing. One (NAMCS data) found a decrease in controlled release oxycodone prescriptions,14 one found a statistically significant decrease in shipments of strong oxycodone but not weak oxycodone (Medicaid data),41 another found a significant decrease in the rise of oxycodone shipments for the PMP group compared with control (ARCOS data),57 and another found a statistically significant increase in controlled release oxycodone prescriptions in PMP states (InVision data).27 The last study found a decrease in logged days of supply of oxycodone (Medicaid data, least years covered).71
Summary of Data Availability and Association of Outcomes With PMPs SUMMARY OF CHANGE IN OPIOID-RELATED OUTCOMES
DOMAIN AND OUTCOME Change in dispensing or prescribing of all opioids Change in dispensing or prescribing of Schedule II opioids Change in dispensing or prescribing of Schedule III opioids Change in dispensing or prescribing of oxycodone Change in dispensing or prescribing of hydrocodone Change in multiple provider use Change in inappropriate prescribing Change in nonmedical prescription opioid use
NO. OF UNIQUE DATA SOURCES/JURISDICTIONS*
DECREASE
NO CHANGE
INCREASE
MIXED
9 3 2 4 3 5 2 2
1 2 0 2 2 2 0 1
6 1 2 0 1 2 2 1
0 0 0 1 0 0 0 0
2y 0 0 1z 0 1x 0 0
*This column reflects the number of studies with unique data sources; in the case where multiple studies use the same data source in the same jurisdictions, the study with the most included years of data has been described. yThere was 1) no difference in PMP states versus non PMP states overall, but a more rapid decrease over time in PMP states; 2) an overall decrease over the study period presented, but no statistical significance provided for the post PMP period. zNo change for weak oxycodone, decrease for strong oxycodone. xDecrease in using ≥3 providers over the past 28 days, no difference in using ≥2 providers over the past 7 days.
ARTICLE IN PRESS Wilson et al Four studies focused on hydrocodone prescriptions and dispensing, with 2 studies that used ARCOS data finding no association41,57 and 2 finding a decrease in 1) hydrocodone prescriptions (InVision data)27 and 2) logged days of supply of hydrocodone (Medicaid data)71 among PMP states. Of the studies reporting on oxycodone and hydrocodone, 4 had a low or moderate risk of bias on all domains, and one was rated as having a low risk of bias on all domains.
Multiple Provider Use Five studies addressed the association of PMPs and multiple provider use, with a total of 7 reported effects.1,17,26,44,50 Two studies reported no significant difference in multiple provider use based on PMP status.1,44 Two studies did not report statistically significant differences in outcomes, with one reporting a 40.1% relative decrease in multiple provider use among seniors and a 32.8% relative decrease in multiple provider use among residents on social assistance after PMP implementation.17 The other reported an increase in the percentage of high dosage buprenorphine that was obtained with overlapping prescriptions before PMP implementation, and a subsequent decline post-PMP implementation.50 Finally, 1 study reported mixed results—a statistically significant decline in using ≥3 different prescribers in a 28-day period was observed after PMP implementation, but no difference was seen in using ≥2 providers in 7 days.26 Three of the 5 studies in this outcome group were rated as having a high risk of bias on the study confounding domain.17,26,50
Potentially Inappropriate Prescribing Two studies examined the association of PMP status with potentially inappropriate prescribing.18,44 These studies reported on 3 different types of potentially inappropriate prescribing: long-term opioid receipt,44 high dosage,44 and a mixed outcome representing prescribing when a patient had signs of dependency, unclear diagnosis, no indication of need, multiple opioids, or the wrong dose or duration.18 Neither study identified a statistically significant difference in the proportion of inappropriate prescribing between PMP and non-PMP jurisdictions. One of the included studies was rated high on multiple risk of bias domains.18
Nonmedical Prescription Opioid Use Three studies report on the association between PMP status and nonmedical prescription opioid use.1,21,54 Two of the 3 studies used data from the National Survey on Drug Use and Health (NSDUH), although they reported on a range of outcomes.1,54 All associations between nonmedical prescription opioid use outcomes and PMP status, including use of all prescription opioids nonmedically, and oxycontin specifically (past month, past year, days of use,-new onset use) were not statistically significant in the studies reporting the most years
The Journal of Pain 7 of National Survey on Drug Use and Health data.1−3 One Canadian study found statistically significant decreases in any past year nonprescription medical opioid use; however, these data were unadjusted and the study was rated as having a high risk of bias on multiple domains.21
Discussion Overall, we found limited evidence to suggest that PMPs were associated with decreased prescribing of schedule II opioids, including oxycodone, and hydrocodone prescribing and dispensing, although the number of unique studies examining each association was small. The relationship between PMP status and prescribing and dispensing of opioids overall, however, is less clear. Nonetheless, decreased schedule II opioid availability is a promising finding, given that this category of drugs has the highest potential for abuse and severe psychological or physical dependence if abused.66 Furthermore, it indicates that PMPs are a valuable intervention in terms of reducing the availability of stronger opioids that may eventually flow into diverted supply. It is possible that, owing to the presence of PMPs, prescribers are moving toward prescribing lower schedules (or less strong opioids). In the case where a study did not account for the strength of opioid prescribed or dispensed and only looked at quantity, a true association between PMP status and opioid dispensing may be masked by a move from stronger to weaker opioids. Most studies of PMP effectiveness come from the United States. Canada is second only to the United States in opioid prescribing, and most Canadian provinces and territories currently have PMPs. However, only 5 Canadian studies were included in this review. The same can be said for European models of PMPs, where only 1 study has been included on this topic, from France.49 The results from these Canadian studies found promising results for the effectiveness of PMPs, with 2 identifying a decrease in multiple provider use,17,26 and one identifying a reduction in nonmedical prescription opioid use.21 The 1 study included from France also observed a decrease in multiple provider use. However, it is important to consider that two of these studies did not include tests of statistical significance for these decreases in multiple provider use. The context of opioid prescribing, dispensing, and use may be different in other countries, and results from American studies may not be generalizable. Future studies should aim to evaluate PMPs in other countries to fill this gap in the literature and to provide evidence for whether PMPs are a valuable piece of other national opioid substances strategies. There was potential bias in many included studies. The most common area where a high risk of bias was reported was study confounding. A study was given a high rating in the study confounding domain if there was no evidence of accounting for important confounders (such as demographic trends, time, other opioid-related interventions in the jurisdictions, and features of PMPs) in the study design or statistical models. This domain was a particular issue with studies assessing the multiple provider use outcome.
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Prescription Monitoring Programs
Accounting for the timing of other interventions was rarely part of analyses, which is an important gap given that PMPs are only part of most jurisdictions’ larger strategies to combat the opioid epidemic. In their recent systematic review, Fink et al20 found evidence that features of PMPs such as a mandatory provider review, provider authorization to access PMP data, frequency of reports, and monitoring of nonscheduled drugs were associated with decreases in overdose deaths; a similar recent study of mandatory provider review, opioids dispensed, and multiple provider episodes, observed similar finding.70 For PMPs to function optimally, providers and dispensers of opioids must access and use PMP data in their practice. Although some statistical models did adjust for PMP features, a future systematic review examining the features of PMPs more closely along with prescribing, dispensing, and other outcomes such as treatment admissions and emergency department visits, may be warranted as the body of evidence continues to expand. PMP exposure, measurement, and outcome measurement were also domains of concern for risk of bias. Some studies did not fully define what constituted a PMP for their study purposes; others measured PMP and/or outcome status on an annual basis, which raised concerns about potential misclassification of PMP status for outcomes during that year (ie, a prescription could have been dispensed before a PMP was implemented, but still marked as occurring during a year where the state had a PMP). Studies that accounted for PMP status more frequently (ie, monthly or quarterly) raised less concerns about misclassification. Many studies examined outcomes at the state level and, as per the ecological fallacy, these findings may not apply to the individual level. Heterogeneity in the unit of analysis, the unit of outcome measurement, study populations, covariate adjustment, and analytic methods used prevented us from performing meta-analyses for any prescribing and dispensing outcomes. This problem is common in systematic reviews of this nature.20,56 Heterogeneity in outcome measurement may also have contributed to inconsistent results across studies for some outcomes. For example, multiple provider use was captured very differently across studies. Two, 3, or ≥4 prescriptions were used as “inappropriate” markers with 7 days, 28 days, and 1 month used as reference periods, as well as varying units of analysis (≤30 months before and after the implementation of a PMP). There was no discernable pattern as to which measures tended to decrease in the presence of a PMP. More important, there was no consensus in the literature around which measures of multiple provider use best indicate a risk to patient safety. Outside of the scope of
this project, those engaged in systematic reviews of studies that are not randomized controlled trials should move toward defining methods for syntheses, reporting and knowledge translation of complex topics such as this one. This systematic review aimed to determine the effect of PMP implementation (initial and over time) on prescribing and dispensing of opioids, but had several limitations. Our literature search did not identify any studies dealing with the impact of PMPs on the coprescribing of opioids with drugs such as benzodiazepines, an important patient safety outcome that should be studied in the future. Furthermore, this review did not focus on the impact of legislative changes to PMP features in jurisdictions where PMPs were already present, such as mandatory registration or participation by health care providers. There is also a need for more recent and robust data in terms of PMP effectiveness. Many of the studies included for outcomes of interest used the same administrative datasets. Although the search strategy identified studies published up to 2018, the most recent year of data in included studies was 2014. Since then, the face of the opioid crisis has markedly changed, shifting from an initial focus on drugs such as oxycodone15 to fentanyl, a drug that is 50 to 100 times more potent than morphine.9,24 There is clearly a need for more up-to-date research in this field. Overall, we observed some evidence that PMPs may enhance patient safety. PMPs were associated with reduced schedule II opioid prescribing and dispensing, as well as multiple provider use. Potentially inappropriate prescribing and nonmedical use of prescription opioids were not investigated commonly in the PMP literature and represent an area for future research. Future studies should broaden their geographic scope to countries other than the United States, use primary data collection or draw on more diverse datasets, seek to synthesize the evidence around PMPs and other monitored drugs, make use of more recent data, and consider the impact of other arms of opioid strategies on opioid-related outcomes.
References
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Acknowledgments We would like to acknowledge Leah Boulos, who developed the search strategy for this study and provided guidance on the systematic review process.
Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.jpain.2019.04.007.
3. Birnbaum HG, White AG, Reynolds JL, Greenberg PE, Zhang M, Vallow S, Schein JR, Katz NP: Estimated costs of
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18. Doyle MJ: An evaluation of the development, implementation and outcome of a pilot prescription monitoring program in Newfoundland and Labrador. PhD diss., Memorial University of Newfoundland, 2004
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19. Dunn KM, Saunders KW, Rutter CM, Banta-Green CJ, Merrill JO, Sullivan MD, Weisner CM, Silverberg MJ, Campbell CI, Psaty BM, Von Korff M V: Overdose and prescribed opioids: Associations among chronic non-cancer pain patients. Ann Intern Med 152:85-92, 2010
6. Brady JE: Prescription drug monitoring and dispensing of prescription opioids. 129:139-147, 2014 7. Brat GA, Agniel D, Beam A, Yorkgitis B, Bicket M, Homer M, Fox KP, Knecht DB, Mcmahill-walraven CN, Palmer N, Kohane I: Postsurgical prescriptions for opioid naive patients and association with overdose and misuse: Retrospective cohort study. BMJ 360:j5790, 2018 8. Canadian Agency for Drugs and Technologies in Health: Policies to Prevent Harms from the Co-Prescribing of Opioids and Central Nervous System Depressant Drugs [Internet]. 2018. Available at: https://cadth.ca/dv/policiesprevent-harms-co-prescribing-opioids-and-central-nervoussystem-depressant-drugs. Accessed March 12, 2019 9. Centers for Disease Control and Prevention: Fentanyl: Overdoses on the rise. Atlanta, GA, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2015 10. Centers for Disease Control and Prevention National Center for Injury Prevention and Control Division of Unintentional Injury Prevention: What states need to know about PMPs. Atlanta, GA, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2017 11. Chang H, Lyapustina T, Rutkow L, Daubresse M, Richey M, Faul M, Stuart EA, Alexander GC: Impact of prescription drug monitoring programs and pill mill laws on high-risk opioid prescribers: A comparative interrupted time series analysis. Drug Alcohol Depend 165:1-8, 2016 12. Christie C, Baker C, Cooper R, Kennedy PJ, Madras B, Bondi P: The President’s Commission on Combatting Drug Addiction and the Opioid Crisis. Washington, DC, United States Government Printing Office, 2017 13. Clarivate Analytics: End Note X8. Philadelphia, PA, Clarivate Analytics, 2017 14. Curtis LH, Stoddard J, Radeva JI, Dans PE, Wright W, Woosley RL, Schulman KA: Geographic variation in the prescription of schedule II opioid analgesics among outpatients in the United States. Health services research 41 (3p1):837-855, 2006 15. Delcher C, Wagenaar AC, Goldberger BA, Cook RL, Maldonado-molina MM: Abrupt decline in oxycodone-caused mortality after implementation of Florida’s prescription drug monitoring program. Drug Alcohol Depend 150:63-68, 2015 16. Donkin K: Drug monitoring won’t address causes of addiction, front-line worker says [Internet]. CBC News. 2017. Available at: https://www.cbc.ca/news/canada/newbrunswick/prescription-monitoring-update-1.4231168. Accessed September 13, 2018 17. Dormuth CR, Miller TA, Huang A, Mamdani MM, Juurlink DN: Effect of a centralized prescription network on
20. Fink DS, Schleimer JP, Sarvet A, Grover KK, Delcher C, Castillo-carniglia A, Kim JH, Rivera-aguirre AE, Henry SG, Martins SS, Cerda M: Association between prescription drug monitoring programs and nonfatal and fatal drug overdoses: A systematic review. Ann Intern Med 168: 783-790, 2018 21. Fischer B, Ialomiteanu A, Kurdyak P, Mann RE, Rehm J: Reductions in non-medical prescription opioid use among adults in Ontario, Canada: Are recent policy interventions working? 2011-2014, 2013 22. Fischer B, Jones W, Krahn M, Rehm J: Differences and over-time changes in levels of prescription opioid analgesic dispensing from retail pharmacies in Canada, 2005−2010. Pharmacoepidemiol Drug Saf 20:1269-1277, 2011 23. Fischer B, Rehm J: Deaths related to the use of prescription opioids. Can Med Assoc J 181:881-882, 2009 24. Fischer B, Russell C, Murphy Y, Kurdyak P: Prescription opioids, abuse and public health in Canada: Is fentanyl the new centre of the opioid crisis ? Pharmacoepidemiol Drug Saf 24:1334-1336, 2015 25. Furlan AD, Frcpc PM, Pellerin D, Shaw K, Bsp DS, Wilson G, Wright J, Furlan AD, Macdougall P, Pellerin D: Overview of four prescription monitoring / review programs in Canada. 19:102-106, 2014 26. Gomes T, Juurlink D, Yao Z, Camacho X, Paterson JM, Singh S, Dhalla I, Sproule B, Mamdani M: Impact of legislation and a prescription monitoring program on the prevalence of potentially inappropriate prescriptions for monitored drugs in Ontario: A time series analysis. Can Med Assoc J Open 2:E256-E261, 2014 27. Goodin AJ: Evaluating State Policy Interventions for Opioid Abuse and Diversion: The Impact on Consumers, Healthcare Providers, and the U.S. Market for Prescription Opioids. Lexington, University of Kentucky, 2015 28. Government of New Brunswick: Frequently Asked Questions: eHealth NB: https://www2.gnb.ca/content/dam/ gnb/Departments/h-s/pdf/en/NBDISPMP/faq_for-healthprofessionals.pdf. Accessed Sept 17, 2018 29. Green TC, Mann MR, Bowman SE, Zaller N, Soto X, Gadea J, Cordy C, Kelly P, Friedmann PD: How does use of a prescription monitoring program change medical practice? Pain Med13:1314-1323, 2012 30. Hansen RN, Oster G, Edelsberg J, Woody GE, Sullivan SD: Economic costs of nonmedical use of prescription opioids. Clin J Pain 27:194-202, 2011 ^ te P, 31. Hayden JA, Van Der Windt DA, Cartwright JL, Co Bombardier C: Assessing bias in studies of prognostic factors. Ann Intern Med 158:280-286, 2013
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32. Healthcare Information and Management Systems Society (HIMSS): Prescription Drug Monitoring Improves Patient Outcomes [Internet]. 2019. Available at: https://www.himss. org/news/prescription-drug-monitoring-improves-patientoutcomes. Accessed March 12, 2019 33. Higgins J, Green S: Cochrane Handbook for Systematic Reviews of Interventions. West Sussex, UK, The Cochrane Collaboration, John Wiley & Sons Ltd., 2008 34. Homant SF: The war on drugs v. the war on pain: Do controlled prescribing laws have a role? Ann Arbor, Western Michigan University, 2006 35. Imtiaz S, Shield KD, Fischer B, Rehm J: Harms of prescription opioid use in the United States. Subst Abuse Treat Prev Policy 9:43-48, 2014 36. International Narcotics Control Board: National drugs technical report: Estimated world requirements for 2013: Statistics for 2011. United Nations, New York, 2012 37. Jones CM, Campopiano M, Baldwin G, Mccance-Katz E: National and state treatment need and capacity for opioid agonist medication-assisted treatment. Am J Public Health 105:e55-e63, 2015 38. Lemire SD, Martner SG, Rn CR: Advanced practice nurses’ use of prescription drug monitoring program information. J Nurse Pract 8:383-388, 2012 39. Lin DH, Lucas E, Murimi IB, Jackson K, Baier M, Frattaroli S, Gielen AC, Moyo P, Simoni-wastila L, Alexander GC: Physician attitudes and experiences with Maryland’s prescription drug monitoring program (PDMP). Addiction 112:311-319, 2016 40. Lin H, Wang Z, Boyd C, Simoni-wastila L, Buu A: Addictive Behaviors Associations between statewide prescription drug monitoring program (PDMP) requirement and physician patterns of prescribing opioid analgesics for patients with non-cancer chronic pain. Addict Behav 76:348-354, 2018 41. Mallatt J: The Effect of Prescription Drug Monitoring Programs on Opioid Prescriptions and Heroin Crime Rates. West Lafayette, IN, 2017. Report No.: 1292 42. Maryland Advisory Council on Prescription Drug Monitoring:Maryland Advisory Council on Prescription Drug Monitoring Legislative Report. Baltimore, MD, Maryland Department of Health, 2009 43. Mcdonald DC, Carlson K, Izarael D: Geographic variation in opioid prescribing in the U.S. J Pain 13:988-996, 2012 44. Meara E, Horwitz JR, Wilson Powell AB, McClelland L, Zhou W, O’Malley J, Morden NE: State legal restrictions and prescription-opioid use among disabled adults. N Engl J Med 375:44-53, 2016 45. Meinhofer A: Prescription drug monitoring programs: The role of asymmetric information on drug availability and abuse. Research Triangle, NC, RTI International, 2017 46. Microsoft: Excel (365). Redmon, WA, Microsoft, 2016 47. Moyo P, Simoni-wastila L, Griffin BA, Onukwugha E, Harrington D, Alexander GC, Palumbo F: Impact of prescription drug monitoring programs (PDMPs) on opioid utilization among Medicare beneficiaries in 10 US States. Addiction 112(10):1784-1796, 2017
Prescription Monitoring Programs 48. National Institute on Drug Abuse: Overdose death rates. Atlanta, GA, Centres for Disease Control, 2018 49. Paulozzi L, Kilbourne E, Desai H: Prescription drug monitoring programs and death rates from drug overdose. Pain Med 12:747-754, 2011 50. Pradel V, Frauger E, Thirion X, Ronfle E, Lapierre V, Masut A, Micallef J: Impact of a prescription monitoring program on doctor-shopping for high dosage buprenorphine. Pharmacoepidemiology and drug safety 18:36-43, 2009 51. Prescription Drug Monitoring Program Training and Technical Assistance Center: Status of PDMPs. 2018. Available at: https://www.pdmpassist.org/pdf/PDMP_Program_Status_20180801.pdf. Accessed Sept 15, 2018 52. Prescription Drug Monitoring Program Training and Technical Assistance Center: Technical assistance guide: History of prescription drug monitoring programs. 2018. Available at: http://www.pdmpassist.org/pdf/PDMP_admin/ TAG_History_PDMPs_final_20180314.pdf. Accessed July 11, 2018 53. Moher D, Liberati A, Tetzlaff J, Altman DG: The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The PRISMA Statement. PLoS Med 6(7): e1000097, 2009. doi:10.1371/journal.pmed1000097 54. Radakrishnan S: Essays in the economics of risky health behaviors (Order No. 3690618). Ithaca, NY, Cornell University, 2015 55. Radomski T, Bixler F, Zickmund S, Roman K, Thorpe C, Hale J, Sileanu F, Hausmann L, Thorpe J, Suda K, Stroupe K, Gordon A, Good C, Fine M, Gellad W: Physicians’ perspectives regarding prescription drug monitoring program use within the Department of Veterans Affairs: A multistate qualitative study. J Gen Intern Med 33:1253-1259, 2018 56. Reeves BC, Higgins JPT, Ramsay C, Shea B, Wells GA: An introduction to methodological issues when including nonrandomised studies in systematic reviews on the effects of interventions. Res Synth Methods 4:1-11, 2013 57. Reisman RM, Shenoy PJ, Atherly AJ, Flowers CR: Prescription opioid usage and abuse relationships: An evaluation of state prescription drug monitoring program efficacy. Subst Abuse 3:41-51, 2009 58. Rittenhouse R, Wei F, Robertson D, Ryan K: Utilization of the Arkansas Prescription Monitoring Program to combat prescription drug abuse. Prev Med Rep 2:524-528, 2015 59. Shield KD, Ialomiteanu A, Fischer B, Rehm J: Assessing the prevalence of non-medical prescription opioid use in the Canadian general adult population: Evidence of large variation depending on survey questions used. BMC Psychiatry 13:1-8, 2013 60. US Department of Justice, Office of Justice Programs, Washington, DC, Bureau of Justice Assistance 61. Simoni-Wastila L, Qian J: Influence of prescription monitoring programs on analgesic utilization by an insured retiree population. Pharmacoepidemiology and drug safety 21(12):1261-1268, 2012 62. Smith RJ, Kilaru AS, Perrone J, Paciotti B, Barg FK, Gadsden SM, Meisel ZF: How, why, and for whom do emergency
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68. Weiner SG, Griggs CA, Mitchell PM, Langlois BK, Friedman FD, Moore RL, Lin SC, Nelson KP, Feldman JA: Clinician impression versus prescription drug monitoring program criteria in the assessment of drug-seeking behavior in the emergency department. Ann Emerg Med 62:281-289, 2013 69. White AG, Birnbaum HG, Mareva MN, Daher M, Vallow S, Schein J, Katz NP: Direct costs of opioid abuse in an insured population in the United States. J Manag Care Pharm 11:469-479, 2005 70. Winstanley EL, Zhang Y, Mashni R, Schnee S, Penm J, Boone J, Mcnamee C, Mackinnon NJ: Mandatory review of a prescription drug monitoring program and impact on opioid and benzodiazepine dispensing. Drug Alcohol Depend 188:169-174, 2018 71. Yarbrough CR: Prescription drug monitoring programs produce a limited impact on painkiller prescribing in Medicare Part D. Health Serv Res: 1-19, 2016
Review Article Effectiveness of Prescription Monitoring Programs in Reducing Opioid Prescribing, Dispensing, and Use Outcomes: A Systematic Review Maria N. Wilson, Jill A. Hayden, Emily Rhodes, Alysia Robinson, and Mark Asbridge Department of Community Health & Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
Abstract: Prescription monitoring programs (PMPs) house and monitor data about the prescribing practices of health care providers, as well as medications received by patients. PMPs aim to promote the appropriate use of prescription opioids by providing this information to prescribers and dispensers. Our objective in this systematic review was to comprehensively identify and assess the available evidence about the impact of PMPs on opioid prescribing and dispensing, multiple provider use for obtaining opioids, inappropriate opioid prescribing, and the extent of nonmedical prescription opioid use. We used a comprehensive search strategy and included study designs that could determine changes in outcomes with the implementation of a PMP. We included 24 studies; 75% of studies were conducted in the United States, and studies encompassed data years from 1993 to 2014. Overall, we did not find evidence to support an association between PMPs and decreased opioid prescribing and dispensing. We found limited, but inconsistent, evidence that PMPs were associated with reduced schedule II opioid prescribing and dispensing, as well as multiple provider use. Covariate adjustment was often inadequate in analyses, as was the timing of outcome and PMP measurement. Future studies should broaden their geographic scope to other countries and use more recent data with standard measurement. Perspective: This systematic review aimed to determine the effectiveness of PMPs in changing prescribing practices and prescription opioid use. The findings from this review will inform policymakers and PMP administrators about the current state of the evidence on program effectiveness. © 2019 by the American Pain Society Key Words: Prescription monitoring programs, opioids, prescription drugs, drug diversion, evaluation. orth America is amid an opioid crisis, which has its roots in opioid prescribing for noncancer pain.37 Each year, 1 in 7 Canadians take a prescribed opioid on the advice of their physician,59 a rate second only to the United States in highest prescribing worldwide.36 In the United States, 2.4 million people meet the criteria for severe opioid use disorder involving dependence on opioid analgesic medications, heroin, or both.37 These high levels of opioid use have led to many problems, including an increase in reported opioid misuse,7 illicit opioid use,35 overdoses and death,4,19 drug diversion, and crime,4,5,23 along with a
N
substantial economic impact.3,30,69 Both Canada and the United States have reported record high levels of opioid overdoses in recent years.48,63 Most jurisdictions in North America have prescription monitoring programs (PMPs).25,28,51,52,64 PMPs are one of several initiatives aimed at promoting the safe and appropriate use of monitored prescription medications.25,52,64 PMPs house data and monitor the prescribing and dispensing practices of health care providers such as physicians and dentists, as well as medications received by patients. They aim to ensure that controlled substances like opioids are prescribed in appropriate
Received January 8, 2019; Revised March 15, 2019; Accepted April 21, 2019. This research was funded by the Canadian Institutes of Health Research Operating Grant: Opioid Crisis Knowledge Synthesis/Subvention (Grant #397982). The authors have no conflicts of interest to declare. Supplementary data accompanying this article are available online at www.jpain.org and www.sciencedirect.com.
Address reprint requests to: Mark Asbridge, Centre for Clinical Research, 5790 University Avenue, R99m 407, Halifax, NS B3H 1V7, Canada. E-mail: [email protected] 1526-5900/$36.00 © 2019 by the American Pain Society https://doi.org/10.1016/j.jpain.2019.04.007
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quantities, following best practice guidelines, are not coprescribed with potentially harmful substances if avoidable (eg, benzodiazepines in the case of opioids8) and are only provided to patients when safe and necessary.52 In so doing, PMPs help to reduce overall exposure to opioids in the population, lower the risk of diversion of supply (the illegal transfer of drugs from a person receiving the prescription to another person), and decrease the number of individuals at risk for subsequent dependence and harm.57 PMPs differ on several characteristics between jurisdictions where they have been implemented, including which substances are monitored and whether health care providers are required to query PMP data before prescribing or dispensing controlled substances. Actively checking patient profiles may allow providers to make a more informed decision on whether to prescribe to a patient based on their prescription history.32 PMPs may also be used to monitor health care provider prescribing practices to note when irregularities in prescribing occur. This information is used, in turn, by PMP managers and regulatory bodies to inform providers of potentially inappropriate prescribing, leading to sanctioning, mentoring, education, and/or training to reduce further occurrences.10 Several direct and indirect benefits of PMPs are described in the literature by health care providers, including the ability to monitor patients’ use history,29,38 improving patient safety,32 increasing comfort levels with prescribing,39 assisting in the recognition of drug-seeking behaviors,38,39 facilitating discussions with patients,29,38,58,62 informing prescription decisions,29,68 and helping providers to moderate the overall amount of opioids they prescribe to patients and within the community.39,58 However, PMPs are not without their criticisms. Barriers to PMP use described in the literature include the administrative burden of checking PMP data55, not seeing the value in PMP information34, and privacy concerns.34 Furthermore, PMPs have been criticized for not addressing the root causes of addiction, and there are concerns that they may hinder the ability to treat pain or drive consumers to black market supply or to more dangerous street opioids such as heroin.12,16 To date, no systematic review has synthesized the evidence on PMP effectiveness in changing outcomes related to opioid prescribing. A review of this nature is warranted given the significant resources that are dedicated to PMPs on an on-going basis: with estimated startup costs of a PMP in the United States ranging between $450,000 and $1.5 million, along with an average annual cost of $500,000 for maintenance.42 This study aims to determine the impact of PMPs on a) opioid prescribing and dispensing, b) multiple provider use for obtaining opioids, c) potentially inappropriate opioid prescribing, and d) nonmedical prescription opioid use.
Methods We used a standard systematic review approach33 and followed the PRISMA reporting guidelines.53
Data Sources and Search Strategy We conducted a comprehensive literature search using several data sources to retrieve all relevant publications. The electronic search strategy was developed using an iterative process in consultation with a medical librarian and was designed to be wide in scope (see Appendix A for database search strategies). We used EndNote X8.1 software (ResearchSoftware.com, Amsterdam, the Netherlands) to de-duplicate search results and manage citations.13 Our search strategy included the following: 1. Electronic searches of the literature in the following databases with the help of an experienced medical librarian searched up to and including January 22, 2018: Medline, Embase, CINAHL, PsycInfo, Web of Science, and Dissertation and Theses Databases. 2. Contacting of authors of key papers. 3. Review of personal libraries of the research team. 4. Search of the grey literature including CADTH, Health Canada, CIHI, CMA Infobase, and a targeted Google search. 5. A manual search of the reference lists of all included studies, related systematic reviews, and all additional relevant reviews identified in the electronic search.
Study Selection We included full published peer-reviewed reports of study data, from any countries, in all languages. Study designs were restricted to comparative studies that could determine changes in outcomes with the implementation of a PMP: pre−post studies, controlled before/after, case control, interrupted time series, or cluster randomized, controlled trial designs.
Population of Interest We included evidence from any jurisdiction (municipal, provincial/state, national) or institutional level (clinic, hospital, system) that had implemented a PMP.
Intervention of Interest We considered the intervention of interest the presence of a PMP, defined as a program that specifically monitors the outpatient prescription dispensing of opioids (or other drugs) by health care providers. PMPs vary considerably in legal structure, the presence and nature of sanctions and penalties, linkages to local governing medical bodies, and the types of data collected on prescriptions, prescribers, and patients. To ensure the broad scope of our review, we included all types of PMPs. To be considered for inclusion, the jurisdiction being studied had to have an operational PMP (not pending or forthcoming). Comparison groups were jurisdictions that had not implemented a PMP. Historical comparisons were included (ie, pre-PMP implementation and post-PMP implementation in the same jurisdiction[s]).
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Outcomes of Interest Across Domains We included 4 main outcomes of interest related to the effectiveness of PMPs compared with no PMPs (historical or comparison jurisdiction[s]): a. Change in the overall volume of opioids prescribed or dispensed or the proportion of specific opioids (eg, oxycodone, hydrocodone) prescribed or dispensed. We included studies that measured per capita, by provider, or by patient profile (defined daily dose per patient or number of patients prescribed). b. Change in rates of multiple provider use, defined as obtaining prescriptions from multiple health care providers in a window of time deemed by the study as inappropriate, either identified with administrative data or self-reported. c. Change in rates of inappropriate prescribing or dispensing practices (ie, not following guidelines for dose or quantity, as defined by the study). d. Change in rates of nonmedical prescription opioid use, either identified with administrative data or self-reported.
Risk of Bias and Assessment of Quality Two reviewers assessed the risk of bias in studies providing evidence about PMP effectiveness using the Quality in Prognostic Studies tool.31 The Quality in Prognostic Studies tool, designed to assess prognostic factor studies, assesses 6 relevant domains of potential bias: study participation, study attrition, prognostic factor measurement (ie, PMP exposure), outcome measurement, study confounding, and statistical analyses and reporting.
Data Extraction For all included studies, data extraction was completed by 2 independent reviewers using pretested data extraction forms developed in Covidence.67 Any discrepancies in data extraction (including risk of bias assessments) were discussed and the assessment of a third reviewer was sought for resolution when necessary. We extracted relevant study details (authors, year, jurisdiction, study design, sample size, response rate) and population characteristics (pharmacists, physicians, dentists), data sources (focus group, survey, administrative) outcomes (including any bivariate and multivariate associations between the outcome and the presence of a PMP), and all variables controlled for.
The Journal of Pain 3 equivalents (MME) and the number of prescriptions in PMP versus non-PMP jurisdictions in this synthesis. In the case of overlapping data across included studies, we present the study results with the most data years available in the primary narrative synthesis. We used Excel 201646 for data management and Stata 1565 for descriptive analyses and calculating pooled estimates.
Results The study selection process is outlined in Fig 1. After screening 2,185 citations at the title level and 968 at the abstract level, as well as conducting a citation search and a search of the grey literature, we screened 161 citations at the full text level. Overall, reviewers were discordant on 6.6% of studies at the title or abstract level, and all conflicts were discussed and resolved. We included a total of 24 studies in the present review related to the impact of PMPs on 1 of our 4 outcomes of interest (Table 1). The most common reason for study exclusion was no or inappropriate comparison jurisdiction (35.8%; Fig 1). These studies either had no comparison jurisdiction (ie, descriptive study of opioid-related outcomes in a PMP jurisdiction only), or the comparison jurisdiction included a PMP (ie, comparing the effect of mandatory vs nonmandatory PMP checking). Eighteen of these studies took place in the United States (75.0%), 5 in Canada (20.8%), and 1 in France (4.2%). The included studies were published between 2004 and 2018, encompassing data years from 1993 to 2014. Five studies (20.8%) reported on outcomes from surveys, and the remaining studies used administrative data (eg, insurance claims). The study population varied from the general population, to insured populations receiving prescriptions, to physicians prescribing opioids (Table 1). Detailed results on associations reported by each study and outcome are found in Appendix C.
Risk of Bias The risk of bias for all included studies is summarized in Table 2 (detailed summary by study in Appendix B). The proportion of studies with low risk of bias by domain were study participation (70.8%), study attrition (91.7%), study confounding (37.5%), PMP exposure and measurement (54.2%), outcome measurement (54.2%), and statistical analysis and reporting (70.8%). Only 1 study was rated as having a low risk of bias on all 6 domains (4.2%), whereas 8 studies had ≥1 domains rated as having a high risk of bias(33.3%).
Data Synthesis Owing to heterogeneity in the presentation of outcomes, we conducted a narrative synthesis of each outcome, considering the strength and consistency of results. For prescribing and dispensing outcomes, we synthesized outcomes on any grouping of opioids monitored (or “specific” opioids) reported in ≥3 publications. In the case of multiple outcome measures available for a study, we extracted all outcomes; however, priority was given to measures that considered morphine milligram
Prescribing and Dispensing Outcomes Results for all prescribing and dispensing outcomes are summarized in Table 3.
Opioid Prescribing and Dispensing Overall Eighteen studies examined the association between PMP status and opioid prescriptions or dispensing (including
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Records idenfied through database searching (n = 3675) Duplicate arcles excluded (n = 1490) Records screened at tle level (n = 2185) Records excluded (n = 1217) Records screened at abstract level (n = 968) Records excluded (n = 817) Records added from citaon search (n = 8) Records added from grey literature search (n = 2)
Full-text arcles assessed for eligibility (n = 161)
Studies included in qualitave synthesis (n = 24)
Full-text arcles excluded, with reasons (n = 137) No/Inappropriate comparison jurisdicon: 49 PMP not present/not part of intervenon: 37 Duplicate publicaon: 12 No primary study data: 10 Inappropriate outcome: 22 Not a full text publicaon: 7
Figure 1. PRISMA diagram for study screening and inclusion process.
shipments).2,6,11,14,18,21,22,27,40,41,43,45,47,49,57,60,61,71 Thirteen of these studies examined overall opioids (ie, not a specific drug or schedule of opioids) prescribed or dispensed, with 3 focusing only on a number of states where a PMP was implemented during the study period, 3 focusing on specific jurisdictions (including 2 from Canada), and 7 focusing on PMP compared with non-PMP states or provinces. Data from these studies examined overall opioids prescribed or dispensed from 5 unique sources, and 9 unique time periods: National Ambulatory Medical Care Survey (NAMCS) 2001 to 2010 and 2012, Automation of Reports and Consolidated Orders System (ARCOS) 2000 to 2013, Medicaid 1999 to 2011, IMS Health 2008 (United States), and 2010 to 2012 (United States, Florida only) and 2005 to 2010 (Canada) and 1997 to 2001 (Canada, Newfoundland only), and the CAMH Monitor 2010 to 2011 (Table 1). Seven studies found no association with opioid prescription outcomes and the presence of a PMP, overall2,21,22,27,40,43 or over time.2,45 Three studies found decreases in opioids prescribed or dispensed in PMP jurisdictions compared with those without PMPs. One study45 observed that opioid prescriptions decreased more rapidly over time in states with a required
use PMP. Another reported a statistically significant decrease in the number of opioids dispensed in the jurisdiction from 1997 to 2001; however, they noted a slight increase in prescriptions from 2000 (the year a PMP was implemented) to 2001, although no measure of statistical significance was presented.18 The third study reported a greater decrease in the number of patients with opioid prescriptions as well as the MME of prescriptions over time among high-risk prescribers (top 5th percentile of opioid prescribers) in Florida compared with Georgia (a non-PMP state at the time).11 No change in these outcomes among low-risk prescribers was observed. Among other included studies that were not part of the primary narrative synthesis, 3 found no statistically significant association between PMP status and opioids prescribed.6,49,71 Brady6 conducted a sensitivity analysis looking at each included state individually, finding a statistically significant decrease in MMEs dispensed per capita in 9 states, no change in 14 states, and a significant increase in 8 states. One study found a decrease in the mean MME of opioids dispensed per month in PMP states versus non-PMP states, but no significant change in the number of prescriptions per month.47
Characteristics of 24 Included Studies on PMP Effectiveness in Changing Prescribing and Dispensing Outcomes STUDY POPULATION
OUTCOME GROUPS
Ali et al1
United States
General population
Bao et al2,* Brady6 Chang et al11,y Curtis et al14 Dormuth et al17,y
United States United States United States United States Canada
Doyle18,y
Canada
Fischer et al22 Fischer et al21,y
Canada Canada
Pain patients in ambulatory care General population State residents who received prescriptions Insured population Provincial residents receiving social assistance or senior citizens, and who visited a pharmacy General population (prescriptions), physicians (potentially inappropriate prescribing) General population General population
Gomes et al26,y Goodin27
Canada United States
Insured population Insured population
Lin et al40,y
United States
Mallatt41,z
United States
McDonald et al43 Meara et al44
United States United States
Ambulatory care patients with noncancer chronic pain General population (ARCOS), insured population (Medicaid) general population Insured population
Meinhofer45 Moyo et al47,*
United States United States
General population Insured population
Prescriptions/dispensing Prescriptions/dispensing
Paulozzi et al49 Pradel et al20,y
United States France
General population Insured population
Prescriptions/dispensing Multiple provider use
Radakrishnan54,* Reisman et al57,y Simeone and Holland60 Simoni-Wastila and Qian61 Yarbrough71,*
United States United States United States United States
General population General population General population Insured population
Nonmedical prescription opioid use Prescriptions/dispensing Prescriptions/dispensing Prescriptions/dispensing
United States
Insured population
Prescriptions/dispensing
Multiple provider use, nonmedical prescription opioid use Prescriptions/dispensing Prescriptions/dispensing Prescriptions/dispensing Prescriptions/dispensing Multiple provider use
Prescriptions/dispensing, potentially inappropriate prescribing Prescriptions/dispensing Prescriptions/dispensing, nonmedical prescription opioid use Multiple provider use Prescriptions/dispensing prescriptions/dispensing Prescriptions/dispensing prescriptions/dispensing Potentially inappropriate prescribing, multiple provider use
Abbreviation: NSDUH, National Survey on Drug Use and Health. *Study only includes states with PMPs implemented during the study period. yStudy only focuses on some PMP states or jurisdictions within a country by choice, owing to analytic constrictions, or owing to availability of data; zARCOS data uses all states, Medicaid uses a subset.
OUTCOME DATA SOURCE, YEARS
UNIT OF ANALYSIS
NSDUH, 2004−2014
State year
NAMCS, 2001−2010 DEA ARCOS, 1999−2008 IMS Health LifeLink LRx database, 2010−2012 AdvancePCS database, 2000 BC Ministry of Health data warehouse, 1993−1997
State year State quarter State month County year Province month
IMS Health Canada database, 1997−2001
Province year
IMS CompuScript database, 2005−2010 Centre for Addiction and Mental Health (CAMH) Monitor (CM); 2010−2011 Ontario Public Drug Benefit Database, 2007−2013 Medicaid Drug Rebate Program data, 1999−2011; i3 InVision Data Mart database; 2007−2009 NAMCS, 2012
Province year Province year
Medicaid State Drug Utilization Data and ARCOS, 2000 −2014 IMS Health LRx database, 2008 Medicare Provider Analysis and Review (MEDPAR), Outpatient, Carrier, and Medicare Beneficiary Summary files, 2006−2012 ARCOS, 2000−2013 Medicare Part D Prescription Drug Event claims, 2008−2012 ARCOS, 1999−2005 General Healthcare Fund reimbursement database, 2000, 2002, 2004, 2005 NSDUH, 2004−2011 ARCOS, 1997−2003 ARCOS, 1997 and 2003 MarketScan COB administrative claims data, 2007 Medicare Part D Prescription Drug Event claims, 2010−2013
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COUNTRY
Province month State year State year State quarter county year State year
State quarter State month State year Regional semester (6 months) State year State year State year State year State year
The Journal of Pain 5
STUDY ID
Wilson et al
Table 1.
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6 The Journal of Pain
Table 2.
Risk of Bias Summary for 24 Included Studies by Quality in Prognostic Studies Domain
DOMAIN RATING Low Moderate High
STUDY PARTICIPATION
STUDY ATTRITION
STUDY CONFOUNDING
PMP MEASUREMENT
OUTCOME MEASUREMENT
STATISTICAL ANALYSIS AND REPORTING
17 7 0
22 2 0
9 7 8
13 11 0
13 10 1
17 3 4
Prescribing and Dispensing of Specific Opioids
units of schedule II opioid MMEs dispensed27 and 2) mean MME of schedule II opioids dispensed per month in PMP states versus non-PMP states.47
Schedule II opioids, schedule III opioids, oxycodone, and hydrocodone were included as drugs of interest for prescribing or dispending outcomes in ≥3 studies. Other drug groupings were not narratively synthesized, as they were present in <3 studies, including buprenorphine,27 methadone,27,60 schedule IV and/or V opioids,47,71 and morphine.57,60
Schedule III Opioids Four studies examined the association between PMPs and schedule III opioid prescribing or dispensing, using 2 unique data sources. Three studies found no statistically significant association, 2 of which used Medicaid data.2,27,71 One study found a slight decrease in the mean MME of schedule III opioids dispensed per month in PMP states versus non-PMP states, also using Medicaid data.47 Studies examining schedule III opioids were rated as having either a low or moderate risk of bias on all domains.
Schedule II Opioids Five studies reported on the association of schedule II opioid prescribing or dispensing and PMP status,2,14,27,47,71 with data from 3 unique sources. All studies adjusted for covariates or accounted for potential confounding by using propensity score matching. Studies examining schedule II opioids were rated as having either a low or moderate risk for bias on all domains. One study found a greater decrease in visits with ≥1 schedule II opioid prescribed over time in PMP states compared with non-PMP states using NAMCS data.2 Another also found a statistically significant decrease in prescription claim rates for schedule II opioids per 1,000 total prescription claims in PMP counties using data from the AdvancePCS database.14 The study covering the most years of ARCOS data found no change in the amount of schedule II opioids prescribed based on PMP status.71 The 2 other studies reported statistically significant decreases in the 1) total Table 3.
Oxycodone and Hydrocodone Five studies examined oxycodone prescriptions and dispensing. One (NAMCS data) found a decrease in controlled release oxycodone prescriptions,14 one found a statistically significant decrease in shipments of strong oxycodone but not weak oxycodone (Medicaid data),41 another found a significant decrease in the rise of oxycodone shipments for the PMP group compared with control (ARCOS data),57 and another found a statistically significant increase in controlled release oxycodone prescriptions in PMP states (InVision data).27 The last study found a decrease in logged days of supply of oxycodone (Medicaid data, least years covered).71
Summary of Data Availability and Association of Outcomes With PMPs SUMMARY OF CHANGE IN OPIOID-RELATED OUTCOMES
DOMAIN AND OUTCOME Change in dispensing or prescribing of all opioids Change in dispensing or prescribing of Schedule II opioids Change in dispensing or prescribing of Schedule III opioids Change in dispensing or prescribing of oxycodone Change in dispensing or prescribing of hydrocodone Change in multiple provider use Change in inappropriate prescribing Change in nonmedical prescription opioid use
NO. OF UNIQUE DATA SOURCES/JURISDICTIONS*
DECREASE
NO CHANGE
INCREASE
MIXED
9 3 2 4 3 5 2 2
1 2 0 2 2 2 0 1
6 1 2 0 1 2 2 1
0 0 0 1 0 0 0 0
2y 0 0 1z 0 1x 0 0
*This column reflects the number of studies with unique data sources; in the case where multiple studies use the same data source in the same jurisdictions, the study with the most included years of data has been described. yThere was 1) no difference in PMP states versus non PMP states overall, but a more rapid decrease over time in PMP states; 2) an overall decrease over the study period presented, but no statistical significance provided for the post PMP period. zNo change for weak oxycodone, decrease for strong oxycodone. xDecrease in using ≥3 providers over the past 28 days, no difference in using ≥2 providers over the past 7 days.
ARTICLE IN PRESS Wilson et al Four studies focused on hydrocodone prescriptions and dispensing, with 2 studies that used ARCOS data finding no association41,57 and 2 finding a decrease in 1) hydrocodone prescriptions (InVision data)27 and 2) logged days of supply of hydrocodone (Medicaid data)71 among PMP states. Of the studies reporting on oxycodone and hydrocodone, 4 had a low or moderate risk of bias on all domains, and one was rated as having a low risk of bias on all domains.
Multiple Provider Use Five studies addressed the association of PMPs and multiple provider use, with a total of 7 reported effects.1,17,26,44,50 Two studies reported no significant difference in multiple provider use based on PMP status.1,44 Two studies did not report statistically significant differences in outcomes, with one reporting a 40.1% relative decrease in multiple provider use among seniors and a 32.8% relative decrease in multiple provider use among residents on social assistance after PMP implementation.17 The other reported an increase in the percentage of high dosage buprenorphine that was obtained with overlapping prescriptions before PMP implementation, and a subsequent decline post-PMP implementation.50 Finally, 1 study reported mixed results—a statistically significant decline in using ≥3 different prescribers in a 28-day period was observed after PMP implementation, but no difference was seen in using ≥2 providers in 7 days.26 Three of the 5 studies in this outcome group were rated as having a high risk of bias on the study confounding domain.17,26,50
Potentially Inappropriate Prescribing Two studies examined the association of PMP status with potentially inappropriate prescribing.18,44 These studies reported on 3 different types of potentially inappropriate prescribing: long-term opioid receipt,44 high dosage,44 and a mixed outcome representing prescribing when a patient had signs of dependency, unclear diagnosis, no indication of need, multiple opioids, or the wrong dose or duration.18 Neither study identified a statistically significant difference in the proportion of inappropriate prescribing between PMP and non-PMP jurisdictions. One of the included studies was rated high on multiple risk of bias domains.18
Nonmedical Prescription Opioid Use Three studies report on the association between PMP status and nonmedical prescription opioid use.1,21,54 Two of the 3 studies used data from the National Survey on Drug Use and Health (NSDUH), although they reported on a range of outcomes.1,54 All associations between nonmedical prescription opioid use outcomes and PMP status, including use of all prescription opioids nonmedically, and oxycontin specifically (past month, past year, days of use,-new onset use) were not statistically significant in the studies reporting the most years
The Journal of Pain 7 of National Survey on Drug Use and Health data.1−3 One Canadian study found statistically significant decreases in any past year nonprescription medical opioid use; however, these data were unadjusted and the study was rated as having a high risk of bias on multiple domains.21
Discussion Overall, we found limited evidence to suggest that PMPs were associated with decreased prescribing of schedule II opioids, including oxycodone, and hydrocodone prescribing and dispensing, although the number of unique studies examining each association was small. The relationship between PMP status and prescribing and dispensing of opioids overall, however, is less clear. Nonetheless, decreased schedule II opioid availability is a promising finding, given that this category of drugs has the highest potential for abuse and severe psychological or physical dependence if abused.66 Furthermore, it indicates that PMPs are a valuable intervention in terms of reducing the availability of stronger opioids that may eventually flow into diverted supply. It is possible that, owing to the presence of PMPs, prescribers are moving toward prescribing lower schedules (or less strong opioids). In the case where a study did not account for the strength of opioid prescribed or dispensed and only looked at quantity, a true association between PMP status and opioid dispensing may be masked by a move from stronger to weaker opioids. Most studies of PMP effectiveness come from the United States. Canada is second only to the United States in opioid prescribing, and most Canadian provinces and territories currently have PMPs. However, only 5 Canadian studies were included in this review. The same can be said for European models of PMPs, where only 1 study has been included on this topic, from France.49 The results from these Canadian studies found promising results for the effectiveness of PMPs, with 2 identifying a decrease in multiple provider use,17,26 and one identifying a reduction in nonmedical prescription opioid use.21 The 1 study included from France also observed a decrease in multiple provider use. However, it is important to consider that two of these studies did not include tests of statistical significance for these decreases in multiple provider use. The context of opioid prescribing, dispensing, and use may be different in other countries, and results from American studies may not be generalizable. Future studies should aim to evaluate PMPs in other countries to fill this gap in the literature and to provide evidence for whether PMPs are a valuable piece of other national opioid substances strategies. There was potential bias in many included studies. The most common area where a high risk of bias was reported was study confounding. A study was given a high rating in the study confounding domain if there was no evidence of accounting for important confounders (such as demographic trends, time, other opioid-related interventions in the jurisdictions, and features of PMPs) in the study design or statistical models. This domain was a particular issue with studies assessing the multiple provider use outcome.
ARTICLE IN PRESS 8 The Journal of Pain
Prescription Monitoring Programs
Accounting for the timing of other interventions was rarely part of analyses, which is an important gap given that PMPs are only part of most jurisdictions’ larger strategies to combat the opioid epidemic. In their recent systematic review, Fink et al20 found evidence that features of PMPs such as a mandatory provider review, provider authorization to access PMP data, frequency of reports, and monitoring of nonscheduled drugs were associated with decreases in overdose deaths; a similar recent study of mandatory provider review, opioids dispensed, and multiple provider episodes, observed similar finding.70 For PMPs to function optimally, providers and dispensers of opioids must access and use PMP data in their practice. Although some statistical models did adjust for PMP features, a future systematic review examining the features of PMPs more closely along with prescribing, dispensing, and other outcomes such as treatment admissions and emergency department visits, may be warranted as the body of evidence continues to expand. PMP exposure, measurement, and outcome measurement were also domains of concern for risk of bias. Some studies did not fully define what constituted a PMP for their study purposes; others measured PMP and/or outcome status on an annual basis, which raised concerns about potential misclassification of PMP status for outcomes during that year (ie, a prescription could have been dispensed before a PMP was implemented, but still marked as occurring during a year where the state had a PMP). Studies that accounted for PMP status more frequently (ie, monthly or quarterly) raised less concerns about misclassification. Many studies examined outcomes at the state level and, as per the ecological fallacy, these findings may not apply to the individual level. Heterogeneity in the unit of analysis, the unit of outcome measurement, study populations, covariate adjustment, and analytic methods used prevented us from performing meta-analyses for any prescribing and dispensing outcomes. This problem is common in systematic reviews of this nature.20,56 Heterogeneity in outcome measurement may also have contributed to inconsistent results across studies for some outcomes. For example, multiple provider use was captured very differently across studies. Two, 3, or ≥4 prescriptions were used as “inappropriate” markers with 7 days, 28 days, and 1 month used as reference periods, as well as varying units of analysis (≤30 months before and after the implementation of a PMP). There was no discernable pattern as to which measures tended to decrease in the presence of a PMP. More important, there was no consensus in the literature around which measures of multiple provider use best indicate a risk to patient safety. Outside of the scope of
this project, those engaged in systematic reviews of studies that are not randomized controlled trials should move toward defining methods for syntheses, reporting and knowledge translation of complex topics such as this one. This systematic review aimed to determine the effect of PMP implementation (initial and over time) on prescribing and dispensing of opioids, but had several limitations. Our literature search did not identify any studies dealing with the impact of PMPs on the coprescribing of opioids with drugs such as benzodiazepines, an important patient safety outcome that should be studied in the future. Furthermore, this review did not focus on the impact of legislative changes to PMP features in jurisdictions where PMPs were already present, such as mandatory registration or participation by health care providers. There is also a need for more recent and robust data in terms of PMP effectiveness. Many of the studies included for outcomes of interest used the same administrative datasets. Although the search strategy identified studies published up to 2018, the most recent year of data in included studies was 2014. Since then, the face of the opioid crisis has markedly changed, shifting from an initial focus on drugs such as oxycodone15 to fentanyl, a drug that is 50 to 100 times more potent than morphine.9,24 There is clearly a need for more up-to-date research in this field. Overall, we observed some evidence that PMPs may enhance patient safety. PMPs were associated with reduced schedule II opioid prescribing and dispensing, as well as multiple provider use. Potentially inappropriate prescribing and nonmedical use of prescription opioids were not investigated commonly in the PMP literature and represent an area for future research. Future studies should broaden their geographic scope to countries other than the United States, use primary data collection or draw on more diverse datasets, seek to synthesize the evidence around PMPs and other monitored drugs, make use of more recent data, and consider the impact of other arms of opioid strategies on opioid-related outcomes.
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Acknowledgments We would like to acknowledge Leah Boulos, who developed the search strategy for this study and provided guidance on the systematic review process.
Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.jpain.2019.04.007.
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