- Email: [email protected]
How does use of a prescription monitoring program change pharmacy practice?
Research
How does use of a prescription monitoring program change pharmacy practice? Traci C. Green, Marita R. Mann, Sarah E. Bowman, Nickolas Zaller, Xaviel Soto, John Gadea Jr., Catherine Cordy, Patrick Kelly, and Peter D. Friedmann
Received May 9, 2012, and in revised form August 31, 2012. Accepted for publication October 15, 2012.
Abstract Objectives: To assess differences in prescription monitoring program (PMP) use between two states with different PMP accessibility (Connecticut [CT] and Rhode Island [RI]), to explore use of PMPs in pharmacy practice, and to examine associations between PMP use and pharmacists’ responses to suspected diversion or “doctor shopping.” Design: Descriptive nonexperimental study. Setting: CT and RI from March through August 2011. Participants: Licensed pharmacists in CT and RI. Intervention: Anonymous surveys e-mailed to pharmacists Main outcome measures: PMP use, use of patient reports in pharmacy practice, and responses to suspected doctor shopping or diversion. Results: Responses from 294 pharmacists were received (CT: 198; RI: 96). PMP users were more likely to use the PMP to detect drug abuse (CT: 79%; RI: 21.9%; P < 0.01) and doctor shopping (67%; 7%; P < 0.01). When faced with suspicious medication use behavior, PMP users were less likely than nonusers to discuss their concerns with the patient (adjusted odds ratio 0.48 [95% CI 0.25–0.92]) but as likely to contact the provider (0.86 [0.21–3.47]), refer the patient back to the prescriber (1.50 [0.79–2.86]), and refuse to fill the prescription (0.63 [0.30–1.30]). PMP users were less likely to state they were out of stock of the drug (0.27 [0.12–0.60]) compared with nonusers. Pharmacists reported high interest in attending continuing education on safe dispensing (72.8%). Conclusion: Pharmacists are important participants in the effort to address prescription drug misuse and abuse. Current PMP use with prevailing systems had limited influence on pharmacy practice. Findings point to future research and needed practice and education innovations to improve patient safety and safer opioid dispensing for pharmacists. Keywords: Prescription monitoring programs, prescription opioids, substance abuse, nonmedical use, pharmacy practice. J Am Pharm Assoc. 2013;53:273–281. doi: 10.1331/JAPhA.2013.12094
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Traci C. Green, PhD, MSc, is Assistant Professor of Emergency Medicine and Epidemiology, Warren Alpert Medical School, Brown University, Providence, RI, and Research Scientist, Department of Emergency Medicine, Rhode Island Hospital, Providence. Marita R. Mann, MPH, is Research Assistant; and Sarah E. Bowman, MPH, is Project Manager, Department of Emergency Medicine, Rhode Island Hospital, Providence. Nickolas Zaller, PhD, is Assistant Professor of Medicine, Warren Alpert Medical School, Brown University, Providence, RI, and Research Scientist, Department of Infectious Diseases, Miriam Hospital, Providence, RI. Xaviel Soto, BS, is Prescription Monitoring Program Coordinator; and John Gadea Jr., PharmD, is Director, Department of Consumer Protection, Hartford, CT. Catherine Cordy, BSPharm, is Director; and Patrick Kelly, PharmD, is Associate Director, Board of Pharmacy, Department of Health, Providence, RI. Peter D. Friedmann, MD, MPH, is Professor of Medicine, Warren Alpert Medical School, Brown University, Providence, RI, and Director, Department of General Internal Medicine, Rhode Island Hospital, Providence. Correspondence: Traci C. Green, PhD, MSc, Rhode Island Hospital, 55 Claverick St., 2nd floor, Providence, RI 02903. Fax: 401-444-2249. E-mail: traci.c.green@ gmail.com Disclosure: The authors declare no conflicts of interest or financial interests in any product or service mentioned in this article, including grants, employment, gifts, stock holdings, or honoraria. Acknowledgments: To Nick Roder-Hanna and George Kenna for reviewing questionnaire drafts. Funding: Grant from the Centers for Disease Control and Prevention (CDC 5R21CE001846)
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I
ncreases in fatal overdose since the mid-1990s have been driven by substantial growth in opioid analgesic prescriptions and nonmedical use of prescription opioids,1–5 among other variables.6 Similarly, opioid-related emergency department visits and hospitalizations have increased during the same period.7–10 In Rhode Island (RI) and Connecticut (CT), overdose has surpassed motor vehicle crashes to become the leading cause of unintentional injury death.11,12 National survey data show that RI has the highest per capita illicit drug use in the country and ranks third in the country for nonmedical use of prescription opioids among individuals 12 years or older, behind Oklahoma and Oregon.13 Prescription monitoring programs (PMPs) are an emerging tool with potential to influence risks to patients associated with abusable medications, especially prescription opioids. PMPs offer more detailed infor-
At a Glance Synopsis: This study provides insight into the mechanisms of how use of an electronic prescription monitoring program (PMP) by pharmacists can influence practice. By surveying pharmacists in Rhode Island and Connecticut, the authors found that PMP use was associated with greater awareness of potential abuse of prescription opioids and less misrepresentation of pharmacy stock to patients when faced with suspicious medication use behavior. However, pharmacists who used the PMP were less likely to discuss concerns about “doctor shopping” or diversion with patients directly. As currently organized and accessed, prevailing PMP systems may limit the extent of the influence of their data on pharmacy practice and patient interactions. Analysis: These results suggest an opportunity to test approaches to improve interactions between pharmacists and patients suspected of doctor shopping and, more generally, improve interactions around abuse of prescription opioid medications. Such approaches could include training, education, interprofessional cooperation, and construction of private counseling areas. The substantial endorsement for continuing pharmacy education in safer prescribing and dispensing of prescription opioid medication suggests that high interest and demand exist for useful tools in handling PMP data, safer opioid prescribing, and dispensing and related topics. Future research could consider interventions to reduce diversion and patient risk that explore use of PMP data by pharmacists as a drug abuse or overdose prevention counseling tool, consider interprofessional cooperation efforts and provider– pharmacist interventions, and test effects of pharmacy education targeted at PMP use and addiction counseling.
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mation than patients themselves or single-institution pharmacy records often provide, permitting verification of patient self-reported prescription history of abusable medications, determination of filling multiple prescriptions of the same drug from multiple providers (i.e., questionable medication behavior or “doctor shopping”), and cataloguing of medications that may suggest contraindications or increased risk of adverse events such as overdose. PMPs exist in 39 states to track prescriptions of controlled medications, and their expanded use is a cornerstone of President Barack Obama’s inaugural National Drug Control Strategy of 2010. Pharmacists are on the front lines of the prescription opioid abuse epidemic. They are the critical link between prescriber and medication and between medication and patient. Pharmacists also are the health professionals most affected by PMPs. Surveys of pharmacists’ attitudes toward PMPs suggest that one of the primary motivations to use them is to decrease diversion opportunities. Fass and Hardigan,14 in their survey of Florida pharmacists, found that a majority across practice settings believed that the PMP would decrease the incidence of doctor shopping, that they would not be discouraged to dispense controlled substances if a PMP was implemented, and that they did not believe that PMP implementation would be an invasion of patient privacy. Ulbrich et al.15 found that community pharmacists were primarily motivated to use the Ohio PMP to “assist with decreasing doctor shopping.” However, little data exist on the effect of PMP use on pharmacy practice patterns. PMPs are available but underused by pharmacists. Most state PMPs report that less than 25% of health professionals use PMPs to obtain patient reports,16 and few states require checking the PMP before dispensing medication.
Objectives The aims of this study were to (1) assess differences in PMP use between two adjacent states with different PMP pharmacist accessibility, (2) explore use of PMPs in pharmacy practice, and (3) examine associations between PMP use and pharmacists’ responses to suspected diversion or doctor shopping.
Methods
CT and RI PMPs Controlled substance data from licensed CT pharmacies are electronically uploaded and securely stored in a central database maintained by the Drug Control Division of the Department of Consumer Protection (DCP). Since July 2008, health professionals licensed to prescribe or dispense controlled substances in CT who have registered with the PMP online system can actively query the PMP database about potential patients’ Schedule II through V prescriptions. The CT PMP patient report is generated within a matter of seconds and readied as a Journal of the American Pharmacists Association
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printable, formatted patient report. The RI PMP cannot be directly accessed or queried by health professionals; it is housed at the RI Department of Health and overseen by the RI Board of Pharmacy. All dispensed Schedule II and III medications are reported to the RI PMP by pharmacies. Inquiries of the RI PMP must be called in, e-mailed, faxed, or mailed to the RI Board of Pharmacy. The RI PMP patient report is generated by manual review of PMP data by RI Board of Pharmacy staff—a process that may take anywhere from hours to weeks to prepare a standardized patient report for the requester. Law enforcement and investigative queries outnumber health professional queries of the RI PMP, whereas health professional queries outnumber law enforcement queries to CT’s PMP. Sample Pharmacists registered with the CT PMP at the time of the survey (n = 210), the CT Pharmacists Association’s membership listserv, CT pharmacists with a registered e-mail address on the Department of Consumer Protection’s communication listserv (e.g., for drug shortages, Food and Drug Administration medication changes), and all RI pharmacists licensed to dispense medications with a registered e-mail address (n = 1731) were sent electronic invitations to respond to the survey. Response rates to mailed and online surveys of health professionals are notoriously low.17–20 Monetary incentives provide only marginal benefit in response rates for this target population21,22 and were not supported by the study budget. Instead, we used, with assistance from the state agencies, a brief introductory e-mail describing survey aims and the importance of pharmacist cooperation and that results would be used to inform PMPs and public health. Survey instrument Survey items (Appendix 1 in the electronic version of this article, available online at www.japha.org) were developed iteratively, in collaboration with PMP staff. Initial items were generated from several sources: a review of the literature, a previous survey,17 consultation with pharmacists, and PMP staff suggestions. The survey was pilot tested with a sample of five pharmacists to verify readability, clarity, and length. We designed the survey to take no longer than 15 minutes to complete. For pharmacists who had queried the PMP, feedback was sought on the reporting process and how PMP data were subsequently used in pharmacy practice (e.g., referred patient to treatment, asked patient to leave pharmacy, ignored report). For RI respondents, we gauged pharmacists’ willingness to use an electronic PMP. All respondents were asked whether and how they currently screen for psychiatric problems and substance abuse, counsel patients on risk factors for overdose, perform overdose symptom recognition and response, and provide guidJournal of the American Pharmacists Association
ance on storage and disposal of unused medications. The survey was anonymous, with deletion of the e-mail and IP address upon sending of the invitation. Responding to the survey was considered evidence of written informed consent. The ZIP Code of the primary pharmacy of practice and a categorical response to number of years practicing pharmacy were the only required fields. Procedure Survey items were programmed into Survey Monkey Professional and hosted by a secure server on the site (www.surveymonkey.com) for 6 months. Survey invitations were e-mailed by the study team with a brief letter explaining the survey’s purpose and the research collaboration with the state agencies. Three motivational reminders were sent to promote higher response rates. At the end of the survey period, responses were downloaded and analyzed. Analysis Descriptive summary statistics were tabulated by state and by PMP use. To compare between states and PMP use experience, chi-square tests and t tests were used. Nonresponse bias was assessed by comparing the demographics of early and late responders. Differences by state in attitudes toward, barriers to, and perceptions of effectiveness in reducing diversion and doctor shopping of PMP use, as well as current practices in overdose prevention counseling, also were conducted using chisquare tests and t tests. Regression analyses examined the association of PMP use on responses to suspected doctor shopping and diversion, controlling for age group, gender, years practicing, screening practices, frequency of dispensing opioids, and state. Analyses were conducted in SAS version 9.2 (SAS Institute, Cary, NC). This study was approved by the Rhode Island Hospital Institutional Review Board. Funding for this study came from the Centers for Disease Control and Prevention (principle investigator: T.C.G.) and had no role in the study’s design, conduct, or reporting.
Results Responses from 294 pharmacists were received (198 in CT and 96 in RI). As a result of duplicates, administrative employee rather than pharmacist e-mail addresses, spam filters, preferences to opt out of SurveyMonkeysponsored surveys, incorrect and defunct e-mail addresses, pharmacists who had relocated or retired, or pharmacists who did not dispense opioids, it was not possible to generate an accurate count of those who received the survey invitation and were eligible to participate. Based on state records of the number of pharmacists and an estimate of the number of pharmacists with e-mail addresses, we estimated an overall response rate of approximately 10% in both states. An estimated response rate of 60.5% response rate (n = 127 of 210) j apha.org
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was obtained among PMP users, based on the number of registered pharmacy users of the CT PMP. Item-level denominators varied, as answers were not required on most items. In terms of nonresponse bias, no differences on demographic or PMP experience were detected between early and late survey responders. Sample characteristics Table 1 reports sample characteristics. Respondents were primarily 45 years or older, and approximately one-half were men. Most pharmacists reported practicing pharmacy for more than 10 years. For the majority of respondents, dispensing of opioids was commonplace; however, less than 20% of respondents currently screen for psychiatric symptoms. Almost 40% of RI respondents and even more CT respondents (54%) reported that they currently “screen” for illicit drug abuse by any assessment method, including professional judgment. Approximately two-thirds of respondents (66.8%) had attended continuing education on safer dispensing of prescription opioids in the previous 5 years. Among those who had not received such education, a similar and high proportion (72.8%) of RI and CT pharmacists were interested in doing so. PMP experience Ever use of the PMP among respondents was 770% higher in CT than in RI (CT 67.9% vs. RI 7.8%; Table 2). Will-
ingness among RI pharmacists to use an electronic PMP like CT’s system was high (89.2%), and among nonusers in both states, there was interest (84.8% in RI, 84.7% in CT) in using the PMP, though awareness of the current system was considerably lower in RI (28.8% vs. 69.6%). Reasons for not currently using the PMP tended to relate to lack of awareness of the system (e.g., not knowing it existed, not knowing how to enroll). Particularly in RI, not knowing that the system existed was the primary deterrent to use (68.8%). Lack of or limited access to the Internet also was a major deterrent to PMP use in both CT and RI (39.2% and 33.8%, respectively); many community pharmacy chains do not permit their staff to access the Internet. In RI, other major concerns were PMP use not being worthwhile unless the pharmacist could directly query the system about a patient (26.0%; i.e., instead of having to submit a query to the Board of Pharmacy) and receipt of the patient report taking too long (23.4%). Lesser concerns in both states included lack of time (23.5% in CT, 19.5% in RI) and available staff (5.9% in CT, 16.9% in RI) to use the system. Approximately 17% of non-PMP users said they did not use the system because it was not required by their employer; only pharmacists in CT mentioned that PMP use was not permitted or discouraged by their employer (9.8% of nonusers). The few RI pharmacists who reported using the PMP accessed it inconsistently in the previous year (57.1% reported ≤10 times), whereas CT pharmacists tended to re-
Table 1. Characteristics of pharmacists responding to survey Rhode Island No. (%)
Characteristic (total no. responses) Age (242), years <35 35–44 45–60 >60 Gender (237) Male Female Time as licensed pharmacist (242), years 1–5 6–10 >10 Frequency of dispensing opioids (294) Several times per day About once per day Multiple times a week Weekly Monthly A few times per year Never Current screening practices Screens for psychiatric symptoms (293) Screens for illicit drug abuse (296) 276 JAPhA | 5 3:3 | M AY/JUN 2013
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Connecticut No. (%)
c2, P
16 of 83 (19.3) 24 of 83 (28.9) 34 of 83 (41.0) 9 of 83 (10.8)
28 of 159 (17.6) 43/159 (27.0) 72 of 159 (45.3) 16 of 159 (10.1)
0.42, 0.94
36 of 81 (44.4) 45 of 81 (55.6)
78 of 156 (50.0) 78 of 156 (50.0)
0.66, 0.41
12 of 83 (14.5) 8 of 83 (9.6) 63 of 83 (75.9)
13 of 159 (8.2) 17 of 159 (10.7) 129 of 159 (81.1)
2.33, 0.31
81 of 96 (84.4) 7 of 96 (7.3) 3 of 96 (3.1) 1 of 96 (1.0) 2 of 96 (2.1) 0 of 198 (0) 5 of 96 (5.2)
167 of 198 (84.3) 7 of 198 (3.5) 3 of 198 (1.5) 3 of 198 (1.5) 0 of 198 (0) 3 of 198 (1.5) 15 of 198 (7.6)
7.11, 0.31
13 of 96 (13.5) 38 of 98 (38.8)
38 of 197 (19.3) 107 of 198 (54.0)
1.48, 0.22 6.11, 0.013
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Table 2. PMP use by surveyed pharmacists Characteristic (total no. responses) Has ever used PMP (277) Times used PMP in past year (129) Never A few (1–10) Monthly Weekly or more often Willingness to use electronic PMP Aware of state PMP (among nonusers of PMP) (139) Interested in using the PMP (among nonusers of PMP) (138) Reasons for not using PMP Did not know system existed (128) Don’t know how to enroll (128) Don’t have Internet access at work or Internet access is limited (128) Not worth it unless query patients Patient report takes too long (128) Don’t have time to access the system (128) Don’t have the staff to access the system (128) My employer/practice does not require it (128) Don’t know how I would use the information (128) My employer /practice does not permit/discourages PMP use (128) I don’t want to have access to this information Don’t have telephone at work or telephone is limited Don’t have time to enroll Knows how misuse was shown on patient report (119) Yes: Red Flag or in text No: Not Indicated, don’t know/not sure
Rhode Island No. (%) 7 of 90 (7.8)
Connecticut No. (%) 127 of 187 (67.9)
1 of 7 (14.3) 4 of 7 (57.1) 0 of 7 (0) 2 of 7 (28.6) 83 of 93 (89.2) 23 of 80 (28.8) 67 of 79 (84.8)
2 of 122 (1.6) 49 of 122 (40.2) 22 of 122 (18.0) 49 of 122 (40.2) — 39 of 56 (69.6) 50 of 59 (84.7)
NA 19.17, <0.01 0.0001, 0.99
53 of 77 (68.8) 33 of 77 (42.9) 26 of 77 (33.8) 20 of 77 (26.0) 18 of 77 (23.4) 15 of 77 (19.5) 13 of 77 (16.9) 11 of 77 (14.3) 7 of 77 (9.1) 0 of 77 (0) 2 of 77 (2.6) 1 of 77 (1.3) —
18 of 51 (35.3) 20 of 51 (39.2) 20 of 51 (39.2) — 0 of 51 (0) 12 of 51 (23.5) 3 of 51 (5.9) 11 of 51 (21.6) 5 of 51 (9.8) 5 of 51 (9.8) 0 of 51 (0) — 4 of 51 (7.8)
13.98, <0.01 0.17, 0.68 0.40, 0.53 NA 13.87, <0.01 0.30, 0.58 3.39, 0.065 1.14, 0.29 0.018, 0.89 7.86, <0.01 1.35, 0.25 NA NA
3 of 6 (50.0) 3 of 6 (50.0)
42 of 113 (37.2) 71 of 113 (62.8)
0.40, 0.53
c2, P 87.98, <0.01 6.50, 0.09
Abbreviations used: NA, not applicable; PMP, prescription monitoring program.
port monthly or more frequent access (58.2%). Approximately one-third of CT pharmacists (37.2%) and one-half (50%) of RI pharmacists who had ever used the PMP correctly identified how misuse of prescription drugs was shown on the patient report. PMP users in CT were no more likely than RI PMP users to have attended continuing pharmacy education on safer prescribing of opioids (72.6% vs. 65.5%, P = 0.09). Characteristics of PMP users PMP users differed from nonusers in several ways and by state. In CT, PMP users were less likely to be women (41.7% vs. 66.0%, P < 0.01), to have practiced pharmacy for less than 10 years (4.8% vs. 14.8% practiced for 1–5 years, 13.3% vs. 5.6% practiced for 6–10 years; P < 0.05), and to screen patients for illicit drug abuse (66.1% vs. 31.7%, P < 0.01). In RI, PMP users were demographically indistinguishable from nonusers. Influences on pharmacy practice Screening for drug abuse. When asked to specify how illicit drug abuse was screened, state-based differences Journal of the American Pharmacists Association
emerged for only one method: PMP use (Table 3). For both states, the most frequently endorsed method of screening for illicit drug abuse was professional judgment; however, in CT, the PMP was mentioned by nearly as many respondents (79.0%) as a tool for screening for drug abuse. The PMP had much higher use endorsement than any of the standardized screening assessments (e.g., CAGE, CRAFFT, DAST [Drug Abuse Screening Test], ASSIST [Alcohol, Smoking and Substance Involvement Screening Test]), all of which were endorsed by less than 3% of respondents, and other survey methods (i.e., asking the patient directly, urine drug screens). Counseling on risks of prescription opioid medications. Pharmacists in both states reported counseling patients prescribed opioid medications at comparable rates and on similar content areas (Table 3). Among the 185 respondents to these items, counseling content primarily included risks of coingestion with alcohol or other central nervous system depressants (78.6%), unauthorized dose escalations (51.4%), risk of addiction (46.3%), sharing medications (44.1%), disposal of medications (37.3%), and symptoms of overdose (37.0%). j apha.org
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Table 3. The PMP and pharmacy practice patterns Characteristic (total no. responses) Screens for illicit drug use by: Professional judgment (137)
Rhode Island 29 of 32 (90.6)
PMP (137) Ask directly (137) Urine drug screen (137) DAST (137) CAGE (137) ASSIST (137) CRAFFT (137) Risks of coingestion (RI: 89; CT: 185)
7 of 32 (21.9) 7 of 32 (21.9) 1 of 32 (3.1) 0 of 32 (0) 0 of 32 (0) 0 of 32 (0) 0 of 32 (0) Nonusers: 69 of 82 (84.2); PMP users: 6 of 7 (85.7) Nonusers: 37 of 82 (45.1); PMP users: 4 of 7 (57.1) Nonusers: 44 of 82 (53.7); PMP users: 5 of 7 (71.4) Nonusers: 36 of 82 (43.9); PMP users: 3 of 7 (42.9) Nonusers: 30 of 82 (36.6); PMP users: 1 of 7 (14.3) Nonusers: 28 of 82 (34.2); PMP users: 4 of 7 (57.1) Nonusers: 22 of 82 (26.8); PMP users: 4 of 7 (57.1) Nonusers: 9 of 82 (11.0); PMP users: 0 of 7 (0) 72 of 89 (80.9)
Risk of addiction (RI: 86; CT: 185) Unauthorized dose escalations (RI: 89; CT: 185) Sharing medications (RI: 89; CT: 185) Symptoms of overdose (RI: 89; CT: 185) Disposal of medications (RI: 89; CT: 185) Storage of medications (RI: 89; CT: 185) None/does not counsel patient (RI: 89; CT: 185) Routinely tries to detect doctor shopping (279) Routinely tries to detect doctor shopping by: Insurance rejection (271) Professional judgment (271) Verify the prescription and prescriber (271) PMP (271) Investigate if contacted (271) Ask directly (271) Electronic pharmacy/prescription database (271) Electronic medical record (271) Does not do anything (271) Aberrant opioid use behavior assessment (271)
67 of 83 (80.7) 63 of 83 (75.9) 60 of 83 (72.3) 6 of 83 (7.2) 36 of 83 (43.4) 27 of 83 (32.5) 23 of 83 (27.7) 8 of 83 (9.6) 3 of 83 (3.6) 0 of 83 (0)
Connecticut 85 of 105 (81.0) 83 of 105 (79.0) 22 of 105 (21.0) 9 of 105 (8.6) 3 of 105 (2.9) 3 of 105 (2.9) 2 of 105 (1.9) 1 of 105 (1.0) Nonusers: 41 of 60 (68.3); PMP users: 99 of 125 (79.2) Nonusers: 25 of 60 (41.7); PMP users: 66 of 125 (52.8) Nonusers: 26 of 60 (43.3); PMP users: 65 of 125 (52.0) Nonusers: 25 of 60 (41.7); PMP users: 62 of 125 (49.6) Nonusers: 22 of 60 (36.7); PMP users: 50 of 125 (40.0) Nonusers: 25 of 60 (41.7); PMP users: 48 of 125 (38.4) Nonusers: 20 of 60 (33.3); PMP users: 37 of 125 (29.6) Nonusers: 14 of 60 (23.3); PMP users: 16 of 125 (12.8) 167 of 190 (87.9) 144 of 188 (76.6) 135 of 188 (71.8) 126 of 188 (67.0) 125 of 188 (66.5) 77 of 188 (41.0) 53 of 188 (28.2) 46 of 188 (24.5) 21 of 188 (11.2) 7 of 188 (3.7) 2 of 188 (1.1)
c2, P 1.64, 0.20 35.57, <0.01 0.13, 0.91 1.08, 0.30 0.93, 0.33 0.93, 0.33 0.62, 0.43 0.31, 0.58 RI: 0.012, 0.91; CT: 2.60, 0.11 RI: 0.38, 0.54; CT: 2.01, 0.16 RI: 0.82, 0.36; CT: 1.22, 0.27 RI: 0.003, 0.96; CT: 1.02, 0.31 RI: 1.41, 0.23; CT: 0.19, 0.66 RI: 1.48, 0.22; CT: 0.18, 0.67 RI: 2.87, 0.09; CT: 0.27, 0.61 RI: 0.85, 0.36; CT: 3.31, 0.07 2.42, 0.12 0.57, 0.45 0.49, 0.48 0.74, 0.39 80.97, <0.01 0.14, 0.71 0.52, 0.47 0.32, 0.57 0.14, 0.71 0.0019, 0.97 0.89, 0.35
Abbreviations used: ASSIST, Alcohol, Smoking and Substance Involvement Screening Test; DAST, Drug Abuse Screening Test; PMP, prescription monitoring program.
Counseling on storage of medications was less common (29.9%). Only 14.8% of respondents reported not counseling patients at all. PMP users had similar counseling practices as nonusers in both states (Table 3). Detecting doctor shopping. Similar proportions of RI and CT respondents reported routinely trying to detect doctor shopping (Table 3). In both states, the methods most often used for detecting doctor shopping included insurance rejection (77.9%), professional judgment (73.1%), and verifying the prescription and prescriber (68.6%). Only one difference in detection practices was observed between states: rates of PMP use to de278 JAPhA | 5 3:3 | M AY/JUN 2013
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tect doctor shopping were more than nine times higher among CT pharmacists than RI pharmacists (66.5% CT, 7.2% RI). Lesser used methods in both states included investigation if contacted (41.7%), asking the patient directly (29.5%), and using an electronic pharmacy/prescription database (25.4%). Perceptions of diversion, doctor shopping, and prescription opioid abuse at practice and state levels. PMP users in CT tended to perceive that the PMP was helpful in reducing diversion in the state and reducing abuse of prescription opioids in their practice and in the state. RI pharmacists did not view that the RI PMP Journal of the American Pharmacists Association
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helped to reduce diversion (77.8%) in the state, abuse of prescription opioids in the pharmacy (81.5%), or abuse of prescription opioids in the state (84%), regardless of their PMP use. In contrast, PMP users in CT had more positive views than nonusers about the PMP’s effects. Compared with nonusers, PMP users viewed that the PMP helped reduce diversion (38.9% vs. 62.3%; χ2 = 7.77, P < 0.01) and prescription opioid abuse (26.9% vs. 43.4%; χ2 = 4.02, P < 0.05) in the state, as well as abuse in their pharmacy practice (25.9% vs. 57.3%, χ2 = 14.00, P < 0.01). CT and RI pharmacists were no different in their perception of diversion or doctor shopping being a common occurrence in their pharmacy (41.2% in CT, 30.9% in RI) or being a serious issue in their pharmacy (50.8% in CT, 46.4% in RI) or in general (90.2% in CT, 86.7% in RI). It is of note that regardless of state and PMP use, pharmacists perceived the problem of diversion and doctor shopping to be relatively common and a serious problem in their own pharmacy; however, diversion and doctor shopping were viewed as a far more serious issue in general. PMP use and responses to suspected diversion or doctor shopping Regression results (Table 4) indicated that few responses to suspected cases of diversion or doctor shopping differed by PMP use, controlling for age group, gender, years practicing, screening practices, frequency of dispensing opioids, and state. In suspected diversion or doctor shopping instances, PMP users were 53% less likely than nonusers to discuss the concerns with the patient and 73% less likely to state to the patient that they were out of stock of the medication. No other statistically significant differences in responses to suspected diversion or doctor shopping by PMP use status were seen in this sample.
Discussion This survey found that pharmacists accessing electronic PMP data tended to use it to screen for abuse and doctor shopping among their patients but that PMP use had limited impact on other aspects of pharmacy practice, such as counseling on safer opioid use, storage, and disposal. The form of the PMP was critical to its uptake: The paper-based PMP in RI was accessed to a far lesser extent than the electronic PMP in CT. Use of an electronic, interactive PMP was associated with greater awareness of the problem of prescription opioid abuse and diversion and perceived utility of the PMP in reducing these problems. These findings indicate that when faced with a suspicious pattern of behavior, PMP users were less likely to discuss their concerns with the patient directly. Avoiding such discussions may be a result of pharmacists’ reluctance to take on the problem of suspected diversion or doctor shopping in their pharmacy, possibly because their physical environment is not equipped to engage in such interactions (e.g., pharmacy lacks private consultaJournal of the American Pharmacists Association
Table 4. Differences in response when suspect diversion/doctor shopping by PMP use
Response Contact the patient’s physician(s) (if known) Discuss the concerns with the patient Refer the patient back to provider Refuse to fill the prescription State out of stock of the drug Counsel the patient on potential overdose risk Refer the patient to substance abuse treatment Ask the patient to leave the pharmacy Notify law enforcement
Typical PMP user actions vs. typical nonuser actions (reference) aOR (95% CI) 0.86 (0.21–3.47) 0.48 (0.25–0.92)a 1.50 (0.79–2.86) 0.63 (0.30–1.30) 0.27 (0.12–0.60)a 0.59 (0.27–1.27) 1.29 (0.25–6.53) 0.46 (0.17–1.29) 0.81 (0.33–2.01)
Abbreviations used: aOR, adjusted odds ratio; PMP, prescription monitoring program. Models are adjusted for age, gender, years practicing pharmacy, drug abuse screening practices, frequency of dispensing opioids, and state. a Statistically significant at P < 0.05.
tion area, limited security if patient becomes violent, no nursing or other support staff to assist with clinical referrals or interventions). In addition, pharmacists may be uncomfortable or insufficiently trained to talk with patients about issues of addiction or may lack time to engage patients. Our survey results suggest an opportunity to test approaches to improve interactions between pharmacists and patients suspected of doctor shopping and, more generally, improve interactions around abuse of prescription opioid medications. Such approaches may include training, education, interprofessional cooperation, and construction of private counseling areas. The substantial endorsement for continuing pharmacy education in safer prescribing and dispensing of prescription opioid medication suggests that high interest and demand exist for useful tools in handling PMP data, safer opioid prescribing, and dispensing and related topics. Although previous studies suggest patterns of understocking of opioid pain relievers in pharmacies in certain neighborhoods,23 in the current study, PMPusing pharmacists were 73% less likely than nonusers to deliberately misrepresent their stock as being out to the patient. This has important implications for patient– pharmacist interactions. One reason why this may be is that the PMP patient report provides the pharmacist with sufficient information to recognize when a suspected diversion/doctor shopping or abuse case has arisen and permits them multiple means of following up with and engaging the patient (e.g., referring to substance j apha.org
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abuse treatment, contacting the prescriber of record, refusing to fill the prescription) rather than deliberately misrepresenting information to the patient. Findings indicate the need for provider education materials and continuing pharmacy education content addressing orientation, registration, and demonstration of how to access a state PMP and how to interpret the patient report and suggested possible actions upon its review. Because use of the electronic PMP was associated with increased screening for drug abuse, equipping pharmacists with the ability to effectively respond to drug abuse is indicated. Important interventions on this topic to consider and test could include an “ask, advise, refer” pharmacist-led intervention for prescription opioid abuse similar to that being tested for tobacco cessation counseling,24 coupling continuing education for prescribers and pharmacists on safer opioid prescribing and addiction with interventions to strengthen patient– pharmacist communications, incentivizing pharmacists with reimbursements to counsel patients by designating them as medication therapy treatment providers,25 and creating pharmacist–substance abuse treatment program collaborative partnerships. Pharmacists could spearhead these efforts by demanding related continuing education, incorporating more addiction education in pharmacy schools, and advocating as needed for appropriate enabling legislation that engages health professionals in efficient and safer prescribing systems. Self-reported counseling on risk of overdose was relatively low in this survey, and pharmacists were no more likely to report counseling patients on risk of overdose when made aware of suspicious behavior. The potential role of PMPs in detecting and intervening with patients at high risk for overdose may be underused.16 For instance, the PMP report could flag coprescription of multiple central nervous system depressant medications to indicate increased overdose risk. PMP program materials rarely address how the PMP patient report could be used for overdose risk recognition or counseling. Future specific interventions could incorporate elements of the PMP patient report into overdose prevention counseling and evaluate its effect on medical and pharmacy practice and overdose mortality. In addition, future research could evaluate the PMP patient report as part of a larger collection of tools for safer dispensing of opioid analgesics or its use in flagging indications for increased safety measures. Such tools may include coprescription of naloxone26–28 through coordination with the prescriber or through establishing a collaborative pharmacy practice agreement for naloxone.29 As currently organized and accessed, however, prevailing PMP systems may limit the extent of the influence of their data on pharmacy practice and the pharmacist–patient interactions. As more states implement electronic, queryable PMPs, with a concomitant expected increase in PMP use by pharmacists, our data suggest that identification 280 JAPhA | 5 3:3 | M AY/JUN 2013
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of patients with drug abuse will increase dramatically. States should consider expanding drug abuse treatment access to meet increases in treatment demand. States with electronic, queryable systems also may want to consider enhancements to their PMPs, such as safety alerts to flag patients at increased risk of addiction for more intense counseling, or linking PMP data to electronic health records to better integrate pharmacists into the health care team. Public funding for state PMPs can be tenuous. For PMPs to be an effective part of the national strategy on prescription opioid abuse and to achieve population-level reductions in diversion, abuse, and overdose mortality, prescribers and pharmacists require reliable, confidential, accessible, and easy-to-use electronic PMPs.
Limitations This study has important limitations. The true denominators for the survey were unknown. Despite concerted efforts to recruit pharmacists, the response rate was low; therefore, the external validity of these findings may be limited. However, the gender and age of survey respondents were comparable with known national pharmacist characteristics,30 and we detected no evidence of survey nonresponse bias. The higher estimated response rate (60.5%) among CT PMP users suggests greater external validity of reported PMP use practices. The behavior of CT pharmacists who did not use the PMP was less well understood. The findings may not pertain to many other states with different PMP systems or pharmacist demographics; in this way, the generalizability of the results may be limited. To protect the anonymity of respondents, we did not ask pharmacists about their practice setting. This is an important variable to consider in future surveys and in educational and training opportunities, as practice setting may influence the frequency with which pharmacists access PMPs and how PMP information obtained is used. A further limitation relates to the small sample size, which may have reduced the power to detect true differences in some comparisons. RI has recently acquired a new, electronic, and directly queryable PMP similar to that of CT. Future surveys with larger numbers of PMP users should be conducted to evaluate changes in RI pharmacists’ awareness, use, and potential practice pharmacy changes associated with this intervention. In addition, this was a cross-sectional study; therefore, associations may not be causal and the temporality of associations cannot be established. The strengths of this survey include the sizeable, two-state sample, the contrast in time (i.e., before implementation of an electronic PMP), and the breadth and detail of the items posed.
Conclusion This survey provides insight into the mechanisms of how use of an electronic PMP by pharmacists may inJournal of the American Pharmacists Association
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fluence practice. PMP use was associated with greater awareness of potential abuse of prescription opioids, direct prescriber rather than patient communication, and less misrepresentation of pharmacy stock to patients when faced with suspicious medication use behavior. Wider use of electronic PMPs by pharmacists may have a more direct influence on opioid abuse and ultimately overdose risk than nonuse or paper-based PMPs. The findings suggest areas for developing interventions and improving pharmacist training to effectively use PMPs to help patients needing additional or specific counseling. References 1. Hall AJ, Logan JE, Toblin RL, et al. Patterns of abuse among unintentional pharmaceutical overdose fatalities. JAMA. 2008;300(22):2613–20. 2. Paulozzi LJ. Opioid analgesic involvement in drug abuse deaths in American metropolitan areas. Am J Public Health. 2006;96(10):1755–7. 3. Paulozzi LJ, Ballesteros MF, Stevens JA. Recent trends in mortality from unintentional injury in the United States. J Safety Res. 2006;37(3):277–83. 4. Paulozzi LJ, Budnitz DS, Xi Y. Increasing deaths from opioid analgesics in the United States. Pharmacoepidemiol Drug Saf. 2006;15(9):618–27. 5. Paulozzi LJ, Ryan GW. Opioid analgesics and rates of fatal drug poisoning in the United States. Am J Prev Med. 2006;31(6):506–11. 6. Shah NG, Lathrop SL, Reichard RR, Landen MG. Unintentional drug overdose death trends in New Mexico, USA, 1990-2005: combinations of heroin, cocaine, prescription opioids and alcohol. Addiction. 2008;103(1):126–36. 7.
Coben JH, Davis SM, Furbee PM, et al. Hospitalizations for poisoning by prescription opioids, sedatives, and tranquilizers. Am J Prev Med. 2010;38(5):517–24.
8. Centers for Disease Control and Prevention. Emergency department visits involving nonmedical use of selected prescription drugs: United States, 2004-2008. MMWR Morb Mortal Wkly Rep. 2010;59(23):705–9. 9. Braden JB, Russo J, Fan MY, et al. Emergency department visits among recipients of chronic opioid therapy. Arch Intern Med. 2010;170(16):1425–32.
15. Ulbrich TR, Dula CA, Green CG, et al. Factors influencing community pharmacists’ enrollment in a state prescription monitoring program. J Am Pharm Assoc. 2010;50(5):588–94. 16. Green TC, Zaller N, Rich J, Bowman S, Friedmann P. Revisiting Paulozzi et al.’s “Prescription drug monitoring programs and death rates from drug overdose.” Pain Med. 2011;12(6):982–5. 17. Anness BA, Willey CJ, Taubman AH. Physician attitudes toward the Rhode Island Duplicate Prescription Law and self-reported prescribing practices for Schedule II medications. Clin Res Regul Aff. 1995;12(4):283–306. 18. Shosteck H, Fairweather WR. Physician response rates to mail and personal interview surveys. Public Opin Q. 1979;43(2):206–17. 19. Shirts BH, Perera S, Hanlon JT, et al. Provider management of and satisfaction with laboratory testing in the nursing home setting: results of a national internet-based survey. J Am Med Dir Assoc. 2009;10(3):161–6.e3. 20. Braithwaite D, Sutton S, Smithson WH, Emery J. Internet-based risk assessment and decision support for the management of familial cancer in primary care: a survey of GPs’ attitudes and intentions. Fam Pract. 2002;19(6):587–90. 21. Halpern SD, Ubel PA, Berlin JA, Asch DA. Randomized trial of 5 dollars versus 10 dollars monetary incentives, envelope size, and candy to increase physician response rates to mailed questionnaires. Med Care. 2002;40(9):834–9. 22. Halpern SD, Asch DA. Commentary: improving response rates to mailed surveys: what do we learn from randomized controlled trials? Int J Epidemiol. 2003;32(4):637–8. 23. Morrison RS, Wallenstein S, Natale DK, et al. “We don’t carry that”: failure of pharmacies in predominantly nonwhite neighborhoods to stock opioid analgesics. N Engl J Med. 2000;342(14):1023–6. 24. Prokhorov AV, Hudmon KS, Marani S, et al. Engaging physicians and pharmacists in providing smoking cessation counseling. Arch Intern Med. 2010;170(18):1640–6. 25. Guglielmo BJ. A prescription for improved chronic disease management: have community pharmacists function at the top of their training: comment on “Engaging physicians and pharmacists in providing smoking cessation counseling.” Arch Intern Med. 2010;170(18):1646–7. 26. Albert S, Brason FW 2nd, Sanford CK, et al. Project Lazarus: community-based overdose prevention in rural North Carolina. Pain Med. 2011;12(suppl 2):S77–85.
10. Wisniewski AM, Purdy CH, Blondell RD. The epidemiologic association between opioid prescribing, non-medical use, and emergency department visits. J Addict Dis. 2008,27(1):1–11.
27. Beletsky L, Ruthazer R, Macalino GE, et al. Physicians’ knowledge of and willingness to prescribe naloxone to reverse accidental opiate overdose: challenges and opportunities. J Urban Health. 2007;84(1):126–36.
11. Green TC, Donnelly EF. Preventable death: accidental drug overdose in Rhode Island. Med Health R I. 2011;94(11):341–3.
28. Rich JD, Green TC, McKenzie MS. Opioids and deaths. N Engl J Med. 2011;364(7):686.
12. Warner M, Chen LH, Makuc DM, et al. Drug poisoning deaths in the United States, 1980–2008. www.cdc.gov/nchs/data/databriefs/ db81.pdf. Accessed March 27, 2013.
29. Green TC, Bowman S, Yokell MA, et al. Collaborative pharmacy practice agreements: a novel approach to expand naloxone access to reduce opioid overdose death. https://apha.confex.com/apha/139am/ webprogram/Paper244785.html. Accessed May 1, 2012.
13. Substance Abuse and Mental Health Services Administration. State estimates of substance use and mental disorders from the 20082009 National Surveys on Drug Use and Health. Rockville, MD: Department of Health & Human Services; 2011.
30. Doucette W, Gaither C, Kreling D, et al. Final report of the 2009 National Pharmacist Workforce Study. Minneapolis, MN: University of Minnesota; 2010.
14. Fass JA, Hardigan PC. Attitudes of Florida pharmacists toward implementing a state prescription drug monitoring program for controlled substances. J Manag Care Pharm. 2011;17(6):430–8. Journal of the American Pharmacists Association
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How does use of a prescription monitoring program change pharmacy practice? Traci C. Green, Marita R. Mann, Sarah E. Bowman, Nickolas Zaller, Xaviel Soto, John Gadea Jr., Catherine Cordy, Patrick Kelly, and Peter D. Friedmann
Received May 9, 2012, and in revised form August 31, 2012. Accepted for publication October 15, 2012.
Abstract Objectives: To assess differences in prescription monitoring program (PMP) use between two states with different PMP accessibility (Connecticut [CT] and Rhode Island [RI]), to explore use of PMPs in pharmacy practice, and to examine associations between PMP use and pharmacists’ responses to suspected diversion or “doctor shopping.” Design: Descriptive nonexperimental study. Setting: CT and RI from March through August 2011. Participants: Licensed pharmacists in CT and RI. Intervention: Anonymous surveys e-mailed to pharmacists Main outcome measures: PMP use, use of patient reports in pharmacy practice, and responses to suspected doctor shopping or diversion. Results: Responses from 294 pharmacists were received (CT: 198; RI: 96). PMP users were more likely to use the PMP to detect drug abuse (CT: 79%; RI: 21.9%; P < 0.01) and doctor shopping (67%; 7%; P < 0.01). When faced with suspicious medication use behavior, PMP users were less likely than nonusers to discuss their concerns with the patient (adjusted odds ratio 0.48 [95% CI 0.25–0.92]) but as likely to contact the provider (0.86 [0.21–3.47]), refer the patient back to the prescriber (1.50 [0.79–2.86]), and refuse to fill the prescription (0.63 [0.30–1.30]). PMP users were less likely to state they were out of stock of the drug (0.27 [0.12–0.60]) compared with nonusers. Pharmacists reported high interest in attending continuing education on safe dispensing (72.8%). Conclusion: Pharmacists are important participants in the effort to address prescription drug misuse and abuse. Current PMP use with prevailing systems had limited influence on pharmacy practice. Findings point to future research and needed practice and education innovations to improve patient safety and safer opioid dispensing for pharmacists. Keywords: Prescription monitoring programs, prescription opioids, substance abuse, nonmedical use, pharmacy practice. J Am Pharm Assoc. 2013;53:273–281. doi: 10.1331/JAPhA.2013.12094
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Traci C. Green, PhD, MSc, is Assistant Professor of Emergency Medicine and Epidemiology, Warren Alpert Medical School, Brown University, Providence, RI, and Research Scientist, Department of Emergency Medicine, Rhode Island Hospital, Providence. Marita R. Mann, MPH, is Research Assistant; and Sarah E. Bowman, MPH, is Project Manager, Department of Emergency Medicine, Rhode Island Hospital, Providence. Nickolas Zaller, PhD, is Assistant Professor of Medicine, Warren Alpert Medical School, Brown University, Providence, RI, and Research Scientist, Department of Infectious Diseases, Miriam Hospital, Providence, RI. Xaviel Soto, BS, is Prescription Monitoring Program Coordinator; and John Gadea Jr., PharmD, is Director, Department of Consumer Protection, Hartford, CT. Catherine Cordy, BSPharm, is Director; and Patrick Kelly, PharmD, is Associate Director, Board of Pharmacy, Department of Health, Providence, RI. Peter D. Friedmann, MD, MPH, is Professor of Medicine, Warren Alpert Medical School, Brown University, Providence, RI, and Director, Department of General Internal Medicine, Rhode Island Hospital, Providence. Correspondence: Traci C. Green, PhD, MSc, Rhode Island Hospital, 55 Claverick St., 2nd floor, Providence, RI 02903. Fax: 401-444-2249. E-mail: traci.c.green@ gmail.com Disclosure: The authors declare no conflicts of interest or financial interests in any product or service mentioned in this article, including grants, employment, gifts, stock holdings, or honoraria. Acknowledgments: To Nick Roder-Hanna and George Kenna for reviewing questionnaire drafts. Funding: Grant from the Centers for Disease Control and Prevention (CDC 5R21CE001846)
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I
ncreases in fatal overdose since the mid-1990s have been driven by substantial growth in opioid analgesic prescriptions and nonmedical use of prescription opioids,1–5 among other variables.6 Similarly, opioid-related emergency department visits and hospitalizations have increased during the same period.7–10 In Rhode Island (RI) and Connecticut (CT), overdose has surpassed motor vehicle crashes to become the leading cause of unintentional injury death.11,12 National survey data show that RI has the highest per capita illicit drug use in the country and ranks third in the country for nonmedical use of prescription opioids among individuals 12 years or older, behind Oklahoma and Oregon.13 Prescription monitoring programs (PMPs) are an emerging tool with potential to influence risks to patients associated with abusable medications, especially prescription opioids. PMPs offer more detailed infor-
At a Glance Synopsis: This study provides insight into the mechanisms of how use of an electronic prescription monitoring program (PMP) by pharmacists can influence practice. By surveying pharmacists in Rhode Island and Connecticut, the authors found that PMP use was associated with greater awareness of potential abuse of prescription opioids and less misrepresentation of pharmacy stock to patients when faced with suspicious medication use behavior. However, pharmacists who used the PMP were less likely to discuss concerns about “doctor shopping” or diversion with patients directly. As currently organized and accessed, prevailing PMP systems may limit the extent of the influence of their data on pharmacy practice and patient interactions. Analysis: These results suggest an opportunity to test approaches to improve interactions between pharmacists and patients suspected of doctor shopping and, more generally, improve interactions around abuse of prescription opioid medications. Such approaches could include training, education, interprofessional cooperation, and construction of private counseling areas. The substantial endorsement for continuing pharmacy education in safer prescribing and dispensing of prescription opioid medication suggests that high interest and demand exist for useful tools in handling PMP data, safer opioid prescribing, and dispensing and related topics. Future research could consider interventions to reduce diversion and patient risk that explore use of PMP data by pharmacists as a drug abuse or overdose prevention counseling tool, consider interprofessional cooperation efforts and provider– pharmacist interventions, and test effects of pharmacy education targeted at PMP use and addiction counseling.
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mation than patients themselves or single-institution pharmacy records often provide, permitting verification of patient self-reported prescription history of abusable medications, determination of filling multiple prescriptions of the same drug from multiple providers (i.e., questionable medication behavior or “doctor shopping”), and cataloguing of medications that may suggest contraindications or increased risk of adverse events such as overdose. PMPs exist in 39 states to track prescriptions of controlled medications, and their expanded use is a cornerstone of President Barack Obama’s inaugural National Drug Control Strategy of 2010. Pharmacists are on the front lines of the prescription opioid abuse epidemic. They are the critical link between prescriber and medication and between medication and patient. Pharmacists also are the health professionals most affected by PMPs. Surveys of pharmacists’ attitudes toward PMPs suggest that one of the primary motivations to use them is to decrease diversion opportunities. Fass and Hardigan,14 in their survey of Florida pharmacists, found that a majority across practice settings believed that the PMP would decrease the incidence of doctor shopping, that they would not be discouraged to dispense controlled substances if a PMP was implemented, and that they did not believe that PMP implementation would be an invasion of patient privacy. Ulbrich et al.15 found that community pharmacists were primarily motivated to use the Ohio PMP to “assist with decreasing doctor shopping.” However, little data exist on the effect of PMP use on pharmacy practice patterns. PMPs are available but underused by pharmacists. Most state PMPs report that less than 25% of health professionals use PMPs to obtain patient reports,16 and few states require checking the PMP before dispensing medication.
Objectives The aims of this study were to (1) assess differences in PMP use between two adjacent states with different PMP pharmacist accessibility, (2) explore use of PMPs in pharmacy practice, and (3) examine associations between PMP use and pharmacists’ responses to suspected diversion or doctor shopping.
Methods
CT and RI PMPs Controlled substance data from licensed CT pharmacies are electronically uploaded and securely stored in a central database maintained by the Drug Control Division of the Department of Consumer Protection (DCP). Since July 2008, health professionals licensed to prescribe or dispense controlled substances in CT who have registered with the PMP online system can actively query the PMP database about potential patients’ Schedule II through V prescriptions. The CT PMP patient report is generated within a matter of seconds and readied as a Journal of the American Pharmacists Association
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printable, formatted patient report. The RI PMP cannot be directly accessed or queried by health professionals; it is housed at the RI Department of Health and overseen by the RI Board of Pharmacy. All dispensed Schedule II and III medications are reported to the RI PMP by pharmacies. Inquiries of the RI PMP must be called in, e-mailed, faxed, or mailed to the RI Board of Pharmacy. The RI PMP patient report is generated by manual review of PMP data by RI Board of Pharmacy staff—a process that may take anywhere from hours to weeks to prepare a standardized patient report for the requester. Law enforcement and investigative queries outnumber health professional queries of the RI PMP, whereas health professional queries outnumber law enforcement queries to CT’s PMP. Sample Pharmacists registered with the CT PMP at the time of the survey (n = 210), the CT Pharmacists Association’s membership listserv, CT pharmacists with a registered e-mail address on the Department of Consumer Protection’s communication listserv (e.g., for drug shortages, Food and Drug Administration medication changes), and all RI pharmacists licensed to dispense medications with a registered e-mail address (n = 1731) were sent electronic invitations to respond to the survey. Response rates to mailed and online surveys of health professionals are notoriously low.17–20 Monetary incentives provide only marginal benefit in response rates for this target population21,22 and were not supported by the study budget. Instead, we used, with assistance from the state agencies, a brief introductory e-mail describing survey aims and the importance of pharmacist cooperation and that results would be used to inform PMPs and public health. Survey instrument Survey items (Appendix 1 in the electronic version of this article, available online at www.japha.org) were developed iteratively, in collaboration with PMP staff. Initial items were generated from several sources: a review of the literature, a previous survey,17 consultation with pharmacists, and PMP staff suggestions. The survey was pilot tested with a sample of five pharmacists to verify readability, clarity, and length. We designed the survey to take no longer than 15 minutes to complete. For pharmacists who had queried the PMP, feedback was sought on the reporting process and how PMP data were subsequently used in pharmacy practice (e.g., referred patient to treatment, asked patient to leave pharmacy, ignored report). For RI respondents, we gauged pharmacists’ willingness to use an electronic PMP. All respondents were asked whether and how they currently screen for psychiatric problems and substance abuse, counsel patients on risk factors for overdose, perform overdose symptom recognition and response, and provide guidJournal of the American Pharmacists Association
ance on storage and disposal of unused medications. The survey was anonymous, with deletion of the e-mail and IP address upon sending of the invitation. Responding to the survey was considered evidence of written informed consent. The ZIP Code of the primary pharmacy of practice and a categorical response to number of years practicing pharmacy were the only required fields. Procedure Survey items were programmed into Survey Monkey Professional and hosted by a secure server on the site (www.surveymonkey.com) for 6 months. Survey invitations were e-mailed by the study team with a brief letter explaining the survey’s purpose and the research collaboration with the state agencies. Three motivational reminders were sent to promote higher response rates. At the end of the survey period, responses were downloaded and analyzed. Analysis Descriptive summary statistics were tabulated by state and by PMP use. To compare between states and PMP use experience, chi-square tests and t tests were used. Nonresponse bias was assessed by comparing the demographics of early and late responders. Differences by state in attitudes toward, barriers to, and perceptions of effectiveness in reducing diversion and doctor shopping of PMP use, as well as current practices in overdose prevention counseling, also were conducted using chisquare tests and t tests. Regression analyses examined the association of PMP use on responses to suspected doctor shopping and diversion, controlling for age group, gender, years practicing, screening practices, frequency of dispensing opioids, and state. Analyses were conducted in SAS version 9.2 (SAS Institute, Cary, NC). This study was approved by the Rhode Island Hospital Institutional Review Board. Funding for this study came from the Centers for Disease Control and Prevention (principle investigator: T.C.G.) and had no role in the study’s design, conduct, or reporting.
Results Responses from 294 pharmacists were received (198 in CT and 96 in RI). As a result of duplicates, administrative employee rather than pharmacist e-mail addresses, spam filters, preferences to opt out of SurveyMonkeysponsored surveys, incorrect and defunct e-mail addresses, pharmacists who had relocated or retired, or pharmacists who did not dispense opioids, it was not possible to generate an accurate count of those who received the survey invitation and were eligible to participate. Based on state records of the number of pharmacists and an estimate of the number of pharmacists with e-mail addresses, we estimated an overall response rate of approximately 10% in both states. An estimated response rate of 60.5% response rate (n = 127 of 210) j apha.org
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was obtained among PMP users, based on the number of registered pharmacy users of the CT PMP. Item-level denominators varied, as answers were not required on most items. In terms of nonresponse bias, no differences on demographic or PMP experience were detected between early and late survey responders. Sample characteristics Table 1 reports sample characteristics. Respondents were primarily 45 years or older, and approximately one-half were men. Most pharmacists reported practicing pharmacy for more than 10 years. For the majority of respondents, dispensing of opioids was commonplace; however, less than 20% of respondents currently screen for psychiatric symptoms. Almost 40% of RI respondents and even more CT respondents (54%) reported that they currently “screen” for illicit drug abuse by any assessment method, including professional judgment. Approximately two-thirds of respondents (66.8%) had attended continuing education on safer dispensing of prescription opioids in the previous 5 years. Among those who had not received such education, a similar and high proportion (72.8%) of RI and CT pharmacists were interested in doing so. PMP experience Ever use of the PMP among respondents was 770% higher in CT than in RI (CT 67.9% vs. RI 7.8%; Table 2). Will-
ingness among RI pharmacists to use an electronic PMP like CT’s system was high (89.2%), and among nonusers in both states, there was interest (84.8% in RI, 84.7% in CT) in using the PMP, though awareness of the current system was considerably lower in RI (28.8% vs. 69.6%). Reasons for not currently using the PMP tended to relate to lack of awareness of the system (e.g., not knowing it existed, not knowing how to enroll). Particularly in RI, not knowing that the system existed was the primary deterrent to use (68.8%). Lack of or limited access to the Internet also was a major deterrent to PMP use in both CT and RI (39.2% and 33.8%, respectively); many community pharmacy chains do not permit their staff to access the Internet. In RI, other major concerns were PMP use not being worthwhile unless the pharmacist could directly query the system about a patient (26.0%; i.e., instead of having to submit a query to the Board of Pharmacy) and receipt of the patient report taking too long (23.4%). Lesser concerns in both states included lack of time (23.5% in CT, 19.5% in RI) and available staff (5.9% in CT, 16.9% in RI) to use the system. Approximately 17% of non-PMP users said they did not use the system because it was not required by their employer; only pharmacists in CT mentioned that PMP use was not permitted or discouraged by their employer (9.8% of nonusers). The few RI pharmacists who reported using the PMP accessed it inconsistently in the previous year (57.1% reported ≤10 times), whereas CT pharmacists tended to re-
Table 1. Characteristics of pharmacists responding to survey Rhode Island No. (%)
Characteristic (total no. responses) Age (242), years <35 35–44 45–60 >60 Gender (237) Male Female Time as licensed pharmacist (242), years 1–5 6–10 >10 Frequency of dispensing opioids (294) Several times per day About once per day Multiple times a week Weekly Monthly A few times per year Never Current screening practices Screens for psychiatric symptoms (293) Screens for illicit drug abuse (296) 276 JAPhA | 5 3:3 | M AY/JUN 2013
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Connecticut No. (%)
c2, P
16 of 83 (19.3) 24 of 83 (28.9) 34 of 83 (41.0) 9 of 83 (10.8)
28 of 159 (17.6) 43/159 (27.0) 72 of 159 (45.3) 16 of 159 (10.1)
0.42, 0.94
36 of 81 (44.4) 45 of 81 (55.6)
78 of 156 (50.0) 78 of 156 (50.0)
0.66, 0.41
12 of 83 (14.5) 8 of 83 (9.6) 63 of 83 (75.9)
13 of 159 (8.2) 17 of 159 (10.7) 129 of 159 (81.1)
2.33, 0.31
81 of 96 (84.4) 7 of 96 (7.3) 3 of 96 (3.1) 1 of 96 (1.0) 2 of 96 (2.1) 0 of 198 (0) 5 of 96 (5.2)
167 of 198 (84.3) 7 of 198 (3.5) 3 of 198 (1.5) 3 of 198 (1.5) 0 of 198 (0) 3 of 198 (1.5) 15 of 198 (7.6)
7.11, 0.31
13 of 96 (13.5) 38 of 98 (38.8)
38 of 197 (19.3) 107 of 198 (54.0)
1.48, 0.22 6.11, 0.013
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Table 2. PMP use by surveyed pharmacists Characteristic (total no. responses) Has ever used PMP (277) Times used PMP in past year (129) Never A few (1–10) Monthly Weekly or more often Willingness to use electronic PMP Aware of state PMP (among nonusers of PMP) (139) Interested in using the PMP (among nonusers of PMP) (138) Reasons for not using PMP Did not know system existed (128) Don’t know how to enroll (128) Don’t have Internet access at work or Internet access is limited (128) Not worth it unless query patients Patient report takes too long (128) Don’t have time to access the system (128) Don’t have the staff to access the system (128) My employer/practice does not require it (128) Don’t know how I would use the information (128) My employer /practice does not permit/discourages PMP use (128) I don’t want to have access to this information Don’t have telephone at work or telephone is limited Don’t have time to enroll Knows how misuse was shown on patient report (119) Yes: Red Flag or in text No: Not Indicated, don’t know/not sure
Rhode Island No. (%) 7 of 90 (7.8)
Connecticut No. (%) 127 of 187 (67.9)
1 of 7 (14.3) 4 of 7 (57.1) 0 of 7 (0) 2 of 7 (28.6) 83 of 93 (89.2) 23 of 80 (28.8) 67 of 79 (84.8)
2 of 122 (1.6) 49 of 122 (40.2) 22 of 122 (18.0) 49 of 122 (40.2) — 39 of 56 (69.6) 50 of 59 (84.7)
NA 19.17, <0.01 0.0001, 0.99
53 of 77 (68.8) 33 of 77 (42.9) 26 of 77 (33.8) 20 of 77 (26.0) 18 of 77 (23.4) 15 of 77 (19.5) 13 of 77 (16.9) 11 of 77 (14.3) 7 of 77 (9.1) 0 of 77 (0) 2 of 77 (2.6) 1 of 77 (1.3) —
18 of 51 (35.3) 20 of 51 (39.2) 20 of 51 (39.2) — 0 of 51 (0) 12 of 51 (23.5) 3 of 51 (5.9) 11 of 51 (21.6) 5 of 51 (9.8) 5 of 51 (9.8) 0 of 51 (0) — 4 of 51 (7.8)
13.98, <0.01 0.17, 0.68 0.40, 0.53 NA 13.87, <0.01 0.30, 0.58 3.39, 0.065 1.14, 0.29 0.018, 0.89 7.86, <0.01 1.35, 0.25 NA NA
3 of 6 (50.0) 3 of 6 (50.0)
42 of 113 (37.2) 71 of 113 (62.8)
0.40, 0.53
c2, P 87.98, <0.01 6.50, 0.09
Abbreviations used: NA, not applicable; PMP, prescription monitoring program.
port monthly or more frequent access (58.2%). Approximately one-third of CT pharmacists (37.2%) and one-half (50%) of RI pharmacists who had ever used the PMP correctly identified how misuse of prescription drugs was shown on the patient report. PMP users in CT were no more likely than RI PMP users to have attended continuing pharmacy education on safer prescribing of opioids (72.6% vs. 65.5%, P = 0.09). Characteristics of PMP users PMP users differed from nonusers in several ways and by state. In CT, PMP users were less likely to be women (41.7% vs. 66.0%, P < 0.01), to have practiced pharmacy for less than 10 years (4.8% vs. 14.8% practiced for 1–5 years, 13.3% vs. 5.6% practiced for 6–10 years; P < 0.05), and to screen patients for illicit drug abuse (66.1% vs. 31.7%, P < 0.01). In RI, PMP users were demographically indistinguishable from nonusers. Influences on pharmacy practice Screening for drug abuse. When asked to specify how illicit drug abuse was screened, state-based differences Journal of the American Pharmacists Association
emerged for only one method: PMP use (Table 3). For both states, the most frequently endorsed method of screening for illicit drug abuse was professional judgment; however, in CT, the PMP was mentioned by nearly as many respondents (79.0%) as a tool for screening for drug abuse. The PMP had much higher use endorsement than any of the standardized screening assessments (e.g., CAGE, CRAFFT, DAST [Drug Abuse Screening Test], ASSIST [Alcohol, Smoking and Substance Involvement Screening Test]), all of which were endorsed by less than 3% of respondents, and other survey methods (i.e., asking the patient directly, urine drug screens). Counseling on risks of prescription opioid medications. Pharmacists in both states reported counseling patients prescribed opioid medications at comparable rates and on similar content areas (Table 3). Among the 185 respondents to these items, counseling content primarily included risks of coingestion with alcohol or other central nervous system depressants (78.6%), unauthorized dose escalations (51.4%), risk of addiction (46.3%), sharing medications (44.1%), disposal of medications (37.3%), and symptoms of overdose (37.0%). j apha.org
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Table 3. The PMP and pharmacy practice patterns Characteristic (total no. responses) Screens for illicit drug use by: Professional judgment (137)
Rhode Island 29 of 32 (90.6)
PMP (137) Ask directly (137) Urine drug screen (137) DAST (137) CAGE (137) ASSIST (137) CRAFFT (137) Risks of coingestion (RI: 89; CT: 185)
7 of 32 (21.9) 7 of 32 (21.9) 1 of 32 (3.1) 0 of 32 (0) 0 of 32 (0) 0 of 32 (0) 0 of 32 (0) Nonusers: 69 of 82 (84.2); PMP users: 6 of 7 (85.7) Nonusers: 37 of 82 (45.1); PMP users: 4 of 7 (57.1) Nonusers: 44 of 82 (53.7); PMP users: 5 of 7 (71.4) Nonusers: 36 of 82 (43.9); PMP users: 3 of 7 (42.9) Nonusers: 30 of 82 (36.6); PMP users: 1 of 7 (14.3) Nonusers: 28 of 82 (34.2); PMP users: 4 of 7 (57.1) Nonusers: 22 of 82 (26.8); PMP users: 4 of 7 (57.1) Nonusers: 9 of 82 (11.0); PMP users: 0 of 7 (0) 72 of 89 (80.9)
Risk of addiction (RI: 86; CT: 185) Unauthorized dose escalations (RI: 89; CT: 185) Sharing medications (RI: 89; CT: 185) Symptoms of overdose (RI: 89; CT: 185) Disposal of medications (RI: 89; CT: 185) Storage of medications (RI: 89; CT: 185) None/does not counsel patient (RI: 89; CT: 185) Routinely tries to detect doctor shopping (279) Routinely tries to detect doctor shopping by: Insurance rejection (271) Professional judgment (271) Verify the prescription and prescriber (271) PMP (271) Investigate if contacted (271) Ask directly (271) Electronic pharmacy/prescription database (271) Electronic medical record (271) Does not do anything (271) Aberrant opioid use behavior assessment (271)
67 of 83 (80.7) 63 of 83 (75.9) 60 of 83 (72.3) 6 of 83 (7.2) 36 of 83 (43.4) 27 of 83 (32.5) 23 of 83 (27.7) 8 of 83 (9.6) 3 of 83 (3.6) 0 of 83 (0)
Connecticut 85 of 105 (81.0) 83 of 105 (79.0) 22 of 105 (21.0) 9 of 105 (8.6) 3 of 105 (2.9) 3 of 105 (2.9) 2 of 105 (1.9) 1 of 105 (1.0) Nonusers: 41 of 60 (68.3); PMP users: 99 of 125 (79.2) Nonusers: 25 of 60 (41.7); PMP users: 66 of 125 (52.8) Nonusers: 26 of 60 (43.3); PMP users: 65 of 125 (52.0) Nonusers: 25 of 60 (41.7); PMP users: 62 of 125 (49.6) Nonusers: 22 of 60 (36.7); PMP users: 50 of 125 (40.0) Nonusers: 25 of 60 (41.7); PMP users: 48 of 125 (38.4) Nonusers: 20 of 60 (33.3); PMP users: 37 of 125 (29.6) Nonusers: 14 of 60 (23.3); PMP users: 16 of 125 (12.8) 167 of 190 (87.9) 144 of 188 (76.6) 135 of 188 (71.8) 126 of 188 (67.0) 125 of 188 (66.5) 77 of 188 (41.0) 53 of 188 (28.2) 46 of 188 (24.5) 21 of 188 (11.2) 7 of 188 (3.7) 2 of 188 (1.1)
c2, P 1.64, 0.20 35.57, <0.01 0.13, 0.91 1.08, 0.30 0.93, 0.33 0.93, 0.33 0.62, 0.43 0.31, 0.58 RI: 0.012, 0.91; CT: 2.60, 0.11 RI: 0.38, 0.54; CT: 2.01, 0.16 RI: 0.82, 0.36; CT: 1.22, 0.27 RI: 0.003, 0.96; CT: 1.02, 0.31 RI: 1.41, 0.23; CT: 0.19, 0.66 RI: 1.48, 0.22; CT: 0.18, 0.67 RI: 2.87, 0.09; CT: 0.27, 0.61 RI: 0.85, 0.36; CT: 3.31, 0.07 2.42, 0.12 0.57, 0.45 0.49, 0.48 0.74, 0.39 80.97, <0.01 0.14, 0.71 0.52, 0.47 0.32, 0.57 0.14, 0.71 0.0019, 0.97 0.89, 0.35
Abbreviations used: ASSIST, Alcohol, Smoking and Substance Involvement Screening Test; DAST, Drug Abuse Screening Test; PMP, prescription monitoring program.
Counseling on storage of medications was less common (29.9%). Only 14.8% of respondents reported not counseling patients at all. PMP users had similar counseling practices as nonusers in both states (Table 3). Detecting doctor shopping. Similar proportions of RI and CT respondents reported routinely trying to detect doctor shopping (Table 3). In both states, the methods most often used for detecting doctor shopping included insurance rejection (77.9%), professional judgment (73.1%), and verifying the prescription and prescriber (68.6%). Only one difference in detection practices was observed between states: rates of PMP use to de278 JAPhA | 5 3:3 | M AY/JUN 2013
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tect doctor shopping were more than nine times higher among CT pharmacists than RI pharmacists (66.5% CT, 7.2% RI). Lesser used methods in both states included investigation if contacted (41.7%), asking the patient directly (29.5%), and using an electronic pharmacy/prescription database (25.4%). Perceptions of diversion, doctor shopping, and prescription opioid abuse at practice and state levels. PMP users in CT tended to perceive that the PMP was helpful in reducing diversion in the state and reducing abuse of prescription opioids in their practice and in the state. RI pharmacists did not view that the RI PMP Journal of the American Pharmacists Association
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helped to reduce diversion (77.8%) in the state, abuse of prescription opioids in the pharmacy (81.5%), or abuse of prescription opioids in the state (84%), regardless of their PMP use. In contrast, PMP users in CT had more positive views than nonusers about the PMP’s effects. Compared with nonusers, PMP users viewed that the PMP helped reduce diversion (38.9% vs. 62.3%; χ2 = 7.77, P < 0.01) and prescription opioid abuse (26.9% vs. 43.4%; χ2 = 4.02, P < 0.05) in the state, as well as abuse in their pharmacy practice (25.9% vs. 57.3%, χ2 = 14.00, P < 0.01). CT and RI pharmacists were no different in their perception of diversion or doctor shopping being a common occurrence in their pharmacy (41.2% in CT, 30.9% in RI) or being a serious issue in their pharmacy (50.8% in CT, 46.4% in RI) or in general (90.2% in CT, 86.7% in RI). It is of note that regardless of state and PMP use, pharmacists perceived the problem of diversion and doctor shopping to be relatively common and a serious problem in their own pharmacy; however, diversion and doctor shopping were viewed as a far more serious issue in general. PMP use and responses to suspected diversion or doctor shopping Regression results (Table 4) indicated that few responses to suspected cases of diversion or doctor shopping differed by PMP use, controlling for age group, gender, years practicing, screening practices, frequency of dispensing opioids, and state. In suspected diversion or doctor shopping instances, PMP users were 53% less likely than nonusers to discuss the concerns with the patient and 73% less likely to state to the patient that they were out of stock of the medication. No other statistically significant differences in responses to suspected diversion or doctor shopping by PMP use status were seen in this sample.
Discussion This survey found that pharmacists accessing electronic PMP data tended to use it to screen for abuse and doctor shopping among their patients but that PMP use had limited impact on other aspects of pharmacy practice, such as counseling on safer opioid use, storage, and disposal. The form of the PMP was critical to its uptake: The paper-based PMP in RI was accessed to a far lesser extent than the electronic PMP in CT. Use of an electronic, interactive PMP was associated with greater awareness of the problem of prescription opioid abuse and diversion and perceived utility of the PMP in reducing these problems. These findings indicate that when faced with a suspicious pattern of behavior, PMP users were less likely to discuss their concerns with the patient directly. Avoiding such discussions may be a result of pharmacists’ reluctance to take on the problem of suspected diversion or doctor shopping in their pharmacy, possibly because their physical environment is not equipped to engage in such interactions (e.g., pharmacy lacks private consultaJournal of the American Pharmacists Association
Table 4. Differences in response when suspect diversion/doctor shopping by PMP use
Response Contact the patient’s physician(s) (if known) Discuss the concerns with the patient Refer the patient back to provider Refuse to fill the prescription State out of stock of the drug Counsel the patient on potential overdose risk Refer the patient to substance abuse treatment Ask the patient to leave the pharmacy Notify law enforcement
Typical PMP user actions vs. typical nonuser actions (reference) aOR (95% CI) 0.86 (0.21–3.47) 0.48 (0.25–0.92)a 1.50 (0.79–2.86) 0.63 (0.30–1.30) 0.27 (0.12–0.60)a 0.59 (0.27–1.27) 1.29 (0.25–6.53) 0.46 (0.17–1.29) 0.81 (0.33–2.01)
Abbreviations used: aOR, adjusted odds ratio; PMP, prescription monitoring program. Models are adjusted for age, gender, years practicing pharmacy, drug abuse screening practices, frequency of dispensing opioids, and state. a Statistically significant at P < 0.05.
tion area, limited security if patient becomes violent, no nursing or other support staff to assist with clinical referrals or interventions). In addition, pharmacists may be uncomfortable or insufficiently trained to talk with patients about issues of addiction or may lack time to engage patients. Our survey results suggest an opportunity to test approaches to improve interactions between pharmacists and patients suspected of doctor shopping and, more generally, improve interactions around abuse of prescription opioid medications. Such approaches may include training, education, interprofessional cooperation, and construction of private counseling areas. The substantial endorsement for continuing pharmacy education in safer prescribing and dispensing of prescription opioid medication suggests that high interest and demand exist for useful tools in handling PMP data, safer opioid prescribing, and dispensing and related topics. Although previous studies suggest patterns of understocking of opioid pain relievers in pharmacies in certain neighborhoods,23 in the current study, PMPusing pharmacists were 73% less likely than nonusers to deliberately misrepresent their stock as being out to the patient. This has important implications for patient– pharmacist interactions. One reason why this may be is that the PMP patient report provides the pharmacist with sufficient information to recognize when a suspected diversion/doctor shopping or abuse case has arisen and permits them multiple means of following up with and engaging the patient (e.g., referring to substance j apha.org
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abuse treatment, contacting the prescriber of record, refusing to fill the prescription) rather than deliberately misrepresenting information to the patient. Findings indicate the need for provider education materials and continuing pharmacy education content addressing orientation, registration, and demonstration of how to access a state PMP and how to interpret the patient report and suggested possible actions upon its review. Because use of the electronic PMP was associated with increased screening for drug abuse, equipping pharmacists with the ability to effectively respond to drug abuse is indicated. Important interventions on this topic to consider and test could include an “ask, advise, refer” pharmacist-led intervention for prescription opioid abuse similar to that being tested for tobacco cessation counseling,24 coupling continuing education for prescribers and pharmacists on safer opioid prescribing and addiction with interventions to strengthen patient– pharmacist communications, incentivizing pharmacists with reimbursements to counsel patients by designating them as medication therapy treatment providers,25 and creating pharmacist–substance abuse treatment program collaborative partnerships. Pharmacists could spearhead these efforts by demanding related continuing education, incorporating more addiction education in pharmacy schools, and advocating as needed for appropriate enabling legislation that engages health professionals in efficient and safer prescribing systems. Self-reported counseling on risk of overdose was relatively low in this survey, and pharmacists were no more likely to report counseling patients on risk of overdose when made aware of suspicious behavior. The potential role of PMPs in detecting and intervening with patients at high risk for overdose may be underused.16 For instance, the PMP report could flag coprescription of multiple central nervous system depressant medications to indicate increased overdose risk. PMP program materials rarely address how the PMP patient report could be used for overdose risk recognition or counseling. Future specific interventions could incorporate elements of the PMP patient report into overdose prevention counseling and evaluate its effect on medical and pharmacy practice and overdose mortality. In addition, future research could evaluate the PMP patient report as part of a larger collection of tools for safer dispensing of opioid analgesics or its use in flagging indications for increased safety measures. Such tools may include coprescription of naloxone26–28 through coordination with the prescriber or through establishing a collaborative pharmacy practice agreement for naloxone.29 As currently organized and accessed, however, prevailing PMP systems may limit the extent of the influence of their data on pharmacy practice and the pharmacist–patient interactions. As more states implement electronic, queryable PMPs, with a concomitant expected increase in PMP use by pharmacists, our data suggest that identification 280 JAPhA | 5 3:3 | M AY/JUN 2013
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of patients with drug abuse will increase dramatically. States should consider expanding drug abuse treatment access to meet increases in treatment demand. States with electronic, queryable systems also may want to consider enhancements to their PMPs, such as safety alerts to flag patients at increased risk of addiction for more intense counseling, or linking PMP data to electronic health records to better integrate pharmacists into the health care team. Public funding for state PMPs can be tenuous. For PMPs to be an effective part of the national strategy on prescription opioid abuse and to achieve population-level reductions in diversion, abuse, and overdose mortality, prescribers and pharmacists require reliable, confidential, accessible, and easy-to-use electronic PMPs.
Limitations This study has important limitations. The true denominators for the survey were unknown. Despite concerted efforts to recruit pharmacists, the response rate was low; therefore, the external validity of these findings may be limited. However, the gender and age of survey respondents were comparable with known national pharmacist characteristics,30 and we detected no evidence of survey nonresponse bias. The higher estimated response rate (60.5%) among CT PMP users suggests greater external validity of reported PMP use practices. The behavior of CT pharmacists who did not use the PMP was less well understood. The findings may not pertain to many other states with different PMP systems or pharmacist demographics; in this way, the generalizability of the results may be limited. To protect the anonymity of respondents, we did not ask pharmacists about their practice setting. This is an important variable to consider in future surveys and in educational and training opportunities, as practice setting may influence the frequency with which pharmacists access PMPs and how PMP information obtained is used. A further limitation relates to the small sample size, which may have reduced the power to detect true differences in some comparisons. RI has recently acquired a new, electronic, and directly queryable PMP similar to that of CT. Future surveys with larger numbers of PMP users should be conducted to evaluate changes in RI pharmacists’ awareness, use, and potential practice pharmacy changes associated with this intervention. In addition, this was a cross-sectional study; therefore, associations may not be causal and the temporality of associations cannot be established. The strengths of this survey include the sizeable, two-state sample, the contrast in time (i.e., before implementation of an electronic PMP), and the breadth and detail of the items posed.
Conclusion This survey provides insight into the mechanisms of how use of an electronic PMP by pharmacists may inJournal of the American Pharmacists Association
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fluence practice. PMP use was associated with greater awareness of potential abuse of prescription opioids, direct prescriber rather than patient communication, and less misrepresentation of pharmacy stock to patients when faced with suspicious medication use behavior. Wider use of electronic PMPs by pharmacists may have a more direct influence on opioid abuse and ultimately overdose risk than nonuse or paper-based PMPs. The findings suggest areas for developing interventions and improving pharmacist training to effectively use PMPs to help patients needing additional or specific counseling. References 1. Hall AJ, Logan JE, Toblin RL, et al. Patterns of abuse among unintentional pharmaceutical overdose fatalities. JAMA. 2008;300(22):2613–20. 2. Paulozzi LJ. Opioid analgesic involvement in drug abuse deaths in American metropolitan areas. Am J Public Health. 2006;96(10):1755–7. 3. Paulozzi LJ, Ballesteros MF, Stevens JA. Recent trends in mortality from unintentional injury in the United States. J Safety Res. 2006;37(3):277–83. 4. Paulozzi LJ, Budnitz DS, Xi Y. Increasing deaths from opioid analgesics in the United States. Pharmacoepidemiol Drug Saf. 2006;15(9):618–27. 5. Paulozzi LJ, Ryan GW. Opioid analgesics and rates of fatal drug poisoning in the United States. Am J Prev Med. 2006;31(6):506–11. 6. Shah NG, Lathrop SL, Reichard RR, Landen MG. Unintentional drug overdose death trends in New Mexico, USA, 1990-2005: combinations of heroin, cocaine, prescription opioids and alcohol. Addiction. 2008;103(1):126–36. 7.
Coben JH, Davis SM, Furbee PM, et al. Hospitalizations for poisoning by prescription opioids, sedatives, and tranquilizers. Am J Prev Med. 2010;38(5):517–24.
8. Centers for Disease Control and Prevention. Emergency department visits involving nonmedical use of selected prescription drugs: United States, 2004-2008. MMWR Morb Mortal Wkly Rep. 2010;59(23):705–9. 9. Braden JB, Russo J, Fan MY, et al. Emergency department visits among recipients of chronic opioid therapy. Arch Intern Med. 2010;170(16):1425–32.
15. Ulbrich TR, Dula CA, Green CG, et al. Factors influencing community pharmacists’ enrollment in a state prescription monitoring program. J Am Pharm Assoc. 2010;50(5):588–94. 16. Green TC, Zaller N, Rich J, Bowman S, Friedmann P. Revisiting Paulozzi et al.’s “Prescription drug monitoring programs and death rates from drug overdose.” Pain Med. 2011;12(6):982–5. 17. Anness BA, Willey CJ, Taubman AH. Physician attitudes toward the Rhode Island Duplicate Prescription Law and self-reported prescribing practices for Schedule II medications. Clin Res Regul Aff. 1995;12(4):283–306. 18. Shosteck H, Fairweather WR. Physician response rates to mail and personal interview surveys. Public Opin Q. 1979;43(2):206–17. 19. Shirts BH, Perera S, Hanlon JT, et al. Provider management of and satisfaction with laboratory testing in the nursing home setting: results of a national internet-based survey. J Am Med Dir Assoc. 2009;10(3):161–6.e3. 20. Braithwaite D, Sutton S, Smithson WH, Emery J. Internet-based risk assessment and decision support for the management of familial cancer in primary care: a survey of GPs’ attitudes and intentions. Fam Pract. 2002;19(6):587–90. 21. Halpern SD, Ubel PA, Berlin JA, Asch DA. Randomized trial of 5 dollars versus 10 dollars monetary incentives, envelope size, and candy to increase physician response rates to mailed questionnaires. Med Care. 2002;40(9):834–9. 22. Halpern SD, Asch DA. Commentary: improving response rates to mailed surveys: what do we learn from randomized controlled trials? Int J Epidemiol. 2003;32(4):637–8. 23. Morrison RS, Wallenstein S, Natale DK, et al. “We don’t carry that”: failure of pharmacies in predominantly nonwhite neighborhoods to stock opioid analgesics. N Engl J Med. 2000;342(14):1023–6. 24. Prokhorov AV, Hudmon KS, Marani S, et al. Engaging physicians and pharmacists in providing smoking cessation counseling. Arch Intern Med. 2010;170(18):1640–6. 25. Guglielmo BJ. A prescription for improved chronic disease management: have community pharmacists function at the top of their training: comment on “Engaging physicians and pharmacists in providing smoking cessation counseling.” Arch Intern Med. 2010;170(18):1646–7. 26. Albert S, Brason FW 2nd, Sanford CK, et al. Project Lazarus: community-based overdose prevention in rural North Carolina. Pain Med. 2011;12(suppl 2):S77–85.
10. Wisniewski AM, Purdy CH, Blondell RD. The epidemiologic association between opioid prescribing, non-medical use, and emergency department visits. J Addict Dis. 2008,27(1):1–11.
27. Beletsky L, Ruthazer R, Macalino GE, et al. Physicians’ knowledge of and willingness to prescribe naloxone to reverse accidental opiate overdose: challenges and opportunities. J Urban Health. 2007;84(1):126–36.
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28. Rich JD, Green TC, McKenzie MS. Opioids and deaths. N Engl J Med. 2011;364(7):686.
12. Warner M, Chen LH, Makuc DM, et al. Drug poisoning deaths in the United States, 1980–2008. www.cdc.gov/nchs/data/databriefs/ db81.pdf. Accessed March 27, 2013.
29. Green TC, Bowman S, Yokell MA, et al. Collaborative pharmacy practice agreements: a novel approach to expand naloxone access to reduce opioid overdose death. https://apha.confex.com/apha/139am/ webprogram/Paper244785.html. Accessed May 1, 2012.
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14. Fass JA, Hardigan PC. Attitudes of Florida pharmacists toward implementing a state prescription drug monitoring program for controlled substances. J Manag Care Pharm. 2011;17(6):430–8. Journal of the American Pharmacists Association
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