This document summarizes a study evaluating the impact of unsolicited reports on prescription drug use. The study compared individuals who received unsolicited reports about questionable prescription patterns to a matched comparison group. It found reductions in several measures of questionable drug use were greater for the unsolicited report group compared to the comparison group, including a statistically significant decline in number of pharmacies visited. However, limitations included difficulty finding a suitable comparison group and incomplete prescription records. Possible next steps proposed expanding the study to address limitations.

1. Prescription
Monitoring Program
Center for Excellence,
Brandeis University
April 10-12, 2012
Walt Disney World Swan Resort

2. Accepted Learning Objectives:
1. Explain the current capabilities and contributions
of PMPs.
2. Describe ways PMP data can be used to predict
patterns of opioid overdoses and how this can be
used to target prevention efforts.
3. Evaluate the benefits of unsolicited reporting as a
means to reduce drug abuse.

3. Disclosure Statement
• All presenters for this session, Thomas
Clark, David R. Hopkins and Leonard
Young, have disclosed no relevant,
real or apparent personal or
professional financial relationships.

4. Massachusetts Department of Public Health
Drug Control Program
Prescription Monitoring Program
An Evaluation of the Impact of
Providing Unsolicited
“Questionable Activity” Reports
to Prescribers
Len Young

5. • Purpose of the MA PMP:
 To help promote safe prescribing and dispensing
 To help prevent drug diversion and abuse.
• MA PMP collects data on Schedule II-V (e.g., narcotic,
stimulant, sedative) prescriptions dispensed by
community, clinic and outpatient pharmacies as well
as out-of-state mail order pharmacies that deliver to
MA.
• Over 11 million Schedule II-V prescription records (8.7
million new prescriptions) were reported to MA PMP in
CY 2011.

6. Estimated Number of Individuals per 100,0001 Showing
Questionable Activity2 by Fiscal Year in MA
7,411
(0.85%)
Individuals
121,238
(5.8%)
Prescriptions
1 Populationincludes all individuals (identified by customer ID) who received at least one Schedule II
opioid prescription in a fiscal year.
2 Questionable activity is defined as having received Schedule II opioid prescriptions from a minimum
of 4 providers and 4 pharmacies during the reported fiscal year.

7. 7

8. WHY SEND UNSOLICITED REPORTS?
• Evidence from PMPs (e.g., Nevada1 and Wyoming2 )
suggests that sending unsolicited reports reduces
questionable activity (e.g., number of prescribers
and pharmacies visited).
• The total economic cost of substance abuse has
reached $245 billion. Includes treatment and
prevention costs, healthcare, losses on job
productivity, crime and social welfare.3
1 PMP Center of Excellence Notes from the Field. Nevada’s Proactive PMP: The Impact of Unsolicited
Reports. October 2011
2 PMP Center of Excellence Notes from the Field. Trends in Wyoming PMP Prescription History Reporting:
Evidence for a Decrease in Doctor Shopping? September 2010.
3 National Institute on Drug Abuse and the National Institute on Alcohol Abuse and Alcoholism in the US.
8

9. Unsolicited reports consist of:
 Overview of initiative
 Patient prescription history
 Provider contact information
 Guidance on use of information
 Two-part optional prescriber survey
 Part 1: Complete upon receipt of report
 Part 2: Three month follow-up

10. • 336 baseline surveys received as of Jan, 2012
• 72% of respondents said Unsolicited Reports (both
electronic and hard copy) “very” or “somewhat
helpful”
• 8% said they were “aware of all or most of other
prescribers” in report
• 9% said “based on current knowledge, including
report, patient appears to have legitimate medical
reason for prescriptions from multiple prescribers”
Source: MDPH and P. Kreiner et al., Brandeis University

11. PURPOSE OF ANALYSIS
• To date, previous analyses (noted in previous slide) of
the impact of unsolicited reports to prescribers have
not included a comparison group.
• MA PMP evaluated the impact of unsolicited reports
on the controlled prescription drug use of individuals
who met specified thresholds of questionable activity
for whom such reports were sent.
• A non-intervention comparison group was included
to provide more accurate measures of the impact of
unsolicited reports.

12. METHODOLOGY
Identifying Individuals for Unsolicited Reports
All individuals considered for the “Unsolicited Report”
group met a pre-specified minimum questionable
activity threshold:
─ Having a Schedule II prescription prescribed by 4 or
more different prescribers
AND
─ Having a Schedule II prescription dispensed at 4 or
more different pharmacies
─ During a 6 month reporting period

13. METHODOLOGY (Continued)
Identifying Individuals for Unsolicited Reports
• Individuals who meet this threshold are considered
to be the at risk population for questionable activity
and comprised the pool of eligible people selected
for an unsolicited report.
• Prescription records of individuals selected for
unsolicited reports were carefully reviewed by
experienced PMP staff to verify that the individual
was exhibiting questionable activity.
• Only a small percentage (< 5 percent) of all the
eligible at risk individuals in MA were selected for an
unsolicited report.

14. METHODOLOGY (Continued)
Identifying Individuals for Unsolicited Reports
• Prescribers received the selected
individual’s Schedule II prescription history
for the most current 1-year time period.
• The individuals (i.e. case population)
included in the analysis were identified
between January and August, 2010.

15. METHODOLOGY (Continued)
Selection of Comparison Group
• A comparison group was selected from the same
pool of eligible individuals who were selected (i.e.,
cases) for unsolicited reports.
• Any individual who was selected for an unsolicited
report was ineligible to be in the comparison group.
• A methodology referred to as “propensity scoring”
was used to identify suitable matches so that key
measures and other characteristics were similar to
the distribution of the case group.

18. *Pre-Intervention data includes all Schedule II prescription records reported to MA PMP
for a specified 12 month time period prior to sending a case report for a specified 12
month time period prior to when the comparison individual is selected.

19. * Excludes individuals where no controlled prescription records were found in the
post-intervention period; cases: n = 62, controls: n = 61
† Statistically significant at p < 0.05

21. CONCLUSIONS
• Findings suggest that providing unsolicited reports reduces key
proxy measures of questionable activity.
• All key measures declined more in the case group compared
with the comparison group, although the differences were, in
most instances, not statistically significant.
• There was a statistically significant difference, between the case
and control group, in the decline in the number of pharmacies
visited from the pre- and post-intervention period.
• The results validate other studies (without a comparison group)
that showed marked declines in measures of questionable
activity for individuals for whom unsolicited reports were sent.

22. STUDY LIMITATIONS
• Difficulty in identifying suitable comparison population.
• MA PMP only collected Schedule II Rx during the pre-
intervention time frame; therefore pre- and post-
intervention comparisons for Schedule III-V are not
possible.
• Limited patient health information - no way to confirm
whether all individuals in the study were engaging in
inappropriate use of controlled drugs; also no way to
follow up on individuals who did not have any controlled
prescription activity during the post-intervention period.
• Incomplete prescription records - some controlled drug
prescription records may not be included in the analysis;
(e.g., individual may intentionally conceal or
misrepresent identifying information.

23. POSSIBLE NEXT STEPS
• Expand study – include larger sample size to improve
power to detect differences.
• Calculate morphine milligram equivalents for pre- and
post-intervention cases and controls.
• Sub group analyses (e.g., individuals who combine
different controlled drug products versus those who
primarily use opioid products; individuals who obtain small
versus large dosage quantities).
• Look at longer time trends - assess whether individuals,
whose use of controlled prescription drugs declined
markedly in the post-intervention time period, maintained
that decline over time.
• Why did the comparison group show such large
decreases in prescriptions, prescribers, pharmacies, etc?

24. Acknowledgement
• Portions of this project were supported by
grants awarded by the U.S. Bureau of
Justice Assistance. Points of view or
opinions in this presentation are those of the
author and do not represent the official
position or policies of the United States
Department of Justice.