Use of Behavioral Insights to Encourage Judicious Prescribing of Opioids
Phase: NA
Conditions: Substance-Related Disorders
Sponsor: University of Southern California

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STUDY PROTOCOL 
  
Behavioral insights to encourage judicious prescribing of opioids: A cluster randomized trial on 
prescribing behaviors for scheduled drugs 
 
 
Principal Investigator:   
  
Jason Doctor, Ph.D., Professor, University of Southern California 
 
 
  
Supported by:  
  
The California Health Care Foundation  
  
 
 
  
 
 
 
 
 
 
 
 
 
 
 
  
 
Date: February 23, 2016 
 
 
 
 
 
 
 

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TABLE OF CONTENTS  
Page 
STUDY TEAM ROSTER ............................................................................................................ 1 
PARTICIPATING STUDY SITES ............................................................................................. 1 
PRÉCIS .......................................................................................................................................... 2 
1. Study objectives ......................................................................................................................... 3 
1.1 Primary Objective ................................................................................................................. 3 
1.2 Secondary Objectives............................................................................................................ 3 
2. BACKGROUND AND RATIONALE .................................................................................... 3 
2.1 Background on Condition, Disease, or Other Primary Study Focus .................................... 3 
2.2 Study Rationale .................................................................... Error! Bookmark not defined. 
3. STUDY DESIGN ....................................................................................................................... 4 
4. SELECTION AND ENROLLMENT OF PARTICIPANTS ................................................ 5 
4.1 Inclusion Criteria .................................................................................................................. 5 
4.2 Exclusion Criteria ................................................................................................................. 5 
4.3 Study Enrollment Procedures ............................................................................................... 5 
5. STUDY INTERVENTIONS .................................................................................................... 5 
5.1 Interventions, Administration, and Duration ........................................................................ 5 
5.2 Handling of Study Interventions ........................................................................................... 6 
5.3 Adherence Assessment ......................................................................................................... 6 
6. STUDY PROCEDURES .......................................................................................................... 6 
6.1 Schedule of Evaluations ....................................................... Error! Bookmark not defined. 
6.2 Description of Evaluations ................................................... Error! Bookmark not defined. 
6.2.1 Screening Evaluation .................................................................................................. 7 
6.2.2 Enrollment, Baseline, and/or Randomization ............................................................. 7 
6.2.3 Follow-up Visits.......................................................................................................... 7 
6.2.4 Completion/Final Evaluation ...................................................................................... 8 
7. SAFETY ASSESSMENTS ....................................................................................................... 8 

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7.1 Specification of Safety Parameters ....................................................................................... 8 
7.2 Methods and Timing for Assessing, Recording, and Analyzing Safety Parameters ............ 8 
7.3 Adverse Events and Serious Adverse Events ....................................................................... 8 
7.4 Reporting Procedures ............................................................................................................ 9 
7.5 Follow-up for Adverse Events .............................................................................................. 9 
7.6 Safety Monitoring ............................................................................................................... 10 
8. INTERVENTION DISCONTINUATION............................................................................ 10 
9. STATISTICAL CONSIDERATIONS .................................................................................. 10 
9.1 General Design Issues ......................................................................................................... 10 
9.2 Sample Size and Randomization ........................................................................................ 10 
9.2.1 Treatment Assignment Procedures ........................................................................... 11 
9.3 Interim analyses and Stopping Rules .................................................................................. 11 
9.4 Outcomes ............................................................................................................................ 12 
9.4.1 Primary outcome ....................................................................................................... 12 
9.4.2 Secondary outcomes ................................................................................................. 12 
9.5 Data Analyses ..................................................................................................................... 12 
10. DATA COLLECTION AND QUALITY ASSURANCE .................................................. 13 
10.1 Data Collection Forms ...................................................................................................... 13 
10.2 Data Management ............................................................................................................. 13 
10.3 Quality Assurance ............................................................................................................. 14 
10.3.1 
Training ................................................................................................................. 14 
10.3.2 
Quality Control Committee ................................................................................... 14 
10.3.3 
Metrics .................................................................................................................. 14 
10.3.4 
Protocol Deviations ............................................................................................... 14 
10.3.5 
Monitoring ............................................................................................................ 14 
11. PARTICIPANT RIGHTS AND CONFIDENTIALITY ................................................... 15 
11.1 Institutional Review Board (IRB) Review ........................................................................ 15 
11.2 Informed Consent Forms .................................................................................................. 15 
11.3 Participant Confidentiality ................................................................................................ 15 
11.4 Study Discontinuation ....................................................................................................... 15 
12. COMMITTEES .................................................................................................................... 15 
13. PUBLICATION OF RESEARCH FINDINGS .................................................................. 15 

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14. REFERENCES ...................................................................................................................... 16 
15. SUPPLEMENTS/APPENDICES ........................................................................................ 16 
 
I. Procedures Schedule 
  
II. Informed Consent Form Template 
 
Not applicable - we will request a waiver of consent.  
 
 
III. Other (add as many appendices as necessary) 

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STUDY TEAM ROSTER  
 
Jason N. Doctor, Ph.D. (Principal Investigator), Leonard D. Schaeffer Center for Health Policy 
and Economics, University of Southern California, 635 Downey Way, Los Angeles, CA 90089-
7273, Office: 213.821.8142, Fax: 213.740.3460, jdoctor@usc.edu 
 
Roneet Lev, MD (Co-Investigator), Scripps Mercy Hospital Emergency Department and San 
Diego County Rx Drug Abuse Medical Task Force, levreviews@gmail.com 
 
Jonathan Lucas, MD (Co-Investigator), San Diego County Medical Examiner’s Office, 5570 
Overland Ave Suite 101, San Diego, CA 92123-1215, Jonathan.Lucas@sdcounty.ca.gov 
 
Tara K. Knight, Ph.D. (Project Manager), Leonard D. Schaeffer Center for Health Policy and 
Economics, University of Southern California, 635 Downey Way, Los Angeles, CA 90089-7273, 
Office: 213.821.7943, Fax: 213.740.3460, knight@usc.edu 
 
Michael Menchine, MD MPH (Co-Investigator), Leonard D. Schaeffer Center for Health Policy 
and Economics, University of Southern California, 635 Downey Way, Los Angeles, CA 90089-
7273, Office: 213.821.7943, Fax: 213.740.3460, mmenchin@gmail.com 
 
  
PARTICIPATING STUDY SITES  
 
San Diego County Medical Examiner’s Office. Jonathan Lucas, MD, Chief Deputy Medical 
Examiner, 5570 Overland Ave Suite 101, San Diego, CA 92123-1215, (PHONE/FAX), 
Jonathan.Lucas@sdcounty.ca.gov 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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PRÉCIS.  
Study Title: Behavioral insights to encourage judicious prescribing of opioids 
Objectives  
In collaboration with the San Diego Medical Examiner’s Office and the State of California’s 
controlled Substance Utilization Review and Evaluation System (CURES), we propose to review 
opioid poisonings over the past 12 months and will send letters to prescribers in San Diego 
County when one of their prescriptions was filled by a patient who died of an opioid poisoning. 
The letters will be non-judgmental and factual, explaining that a patient of theirs who was being 
treated with prescription narcotics died of an opioid poisoning. The letter will also encourage 
judicious prescribing including use of the CURES system before prescribing. We will evaluate 
physician prescribing practices over 24 months (12 months pre- and 12 months post-letter) using 
data from the CURES database. Our hypothesis is that letters will make the risk of opioids more 
cognitively available and that physicians will respond by prescribing them more carefully. This 
will result in fewer deaths due to misuse and more frequent use of the CURES system. 
Design and Outcomes   
We will conduct a cluster randomized trial of an informative intervention targeting the over-
prescription of scheduled drugs with patient death as the unit of randomization.  
The follow outcomes will be analyzed: 
1) Fewer opioid prescriptions written among clinicians receiving the letter, compared to controls;  
2) Lower cumulative Morphine Equivalent Dose (cMED) among clinicians receiving the letter 
(as indicated by dosing on prescriptions filled);  
3) More frequent use of CURES among clinicians receiving a letter;  
4) Fewer opioid prescriptions for ≥ 100 milligram morphine equivalent (MME) 
5) Fewer benzodiazepine co-prescriptions 
In the main analysis, we will compare a 12-month baseline period to a 12-month post-treatment 
follow-up period. 
Interventions and Duration  
The intervention will be a one-time treatment for all participants, with a three-month and a one 
year follow-up period to measure the persistence of the effects after the intervention.  
The following intervention will be analyzed: 
A courtesy communication informing prescribers of the death of a former patient where 
prescription drug overdose was the primary cause or contributed to the cause of the death. The 
letter will invite prescribers to review materials regarding the clinical guidelines from the Center 
of Disease Control, the California Department of Public Health, and the San Diego County 
Prescription Drug Abuse Medical Task Force for a more prudent prescription of those drugs. 

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Sample Size and Population  
Our sample will consist of a heterogeneous group of clinicians and allied health professionals 
with scheduled drug prescribing privileges practicing in San Diego County who prescribed a 
Schedule II-IV drug (i.e., drugs as defined by the United States Controlled Substances Act as 
having an abuse potential, but also a currently accepted medical use) to a person who died as a 
result of a Schedule II-IV accidental drug death. 
 
1. STUDY OBJECTIVES 
1.1 Primary Objective  
Our primary objective is to analyze prescribers in the treatment group’s milligram morphine 
equivalent dose (mg MED) compared to the control participants before and after the intervention. 
1.2 Secondary Objectives 
Our secondary objective is to observe frequency of CURES use and the number of “new starts”. 
2. BACKGROUND AND RATIONALE  
2.1 Background on Condition, Disease, or Other Primary Study Focus 
Much of the increase in opioid prescribing rates from 1999 until the past decade has been driven 
by an increase in the use of prescription opioids to treat non-cancer pain (Guy et al. 2017; 
Boudreau et al. 2009). Although opioids carry significant risks of overdose and addiction, they 
are no more effective for treating chronic non-cancerous pain over a one year period than non-
opioid alternatives (Krebs et al. 2018). The greater availability of prescription opioids has been 
accompanied by an alarming rise in the negative consequences related to opioid use. In 2017, 
there were 17,029 prescription opioid overdose deaths in the US (Scholl 2019). The costs of 
prescription opioid adverse outcomes are staggering. Aggregate costs for prescription opioid 
harms are estimated at over $78.5 billion (in 2013 USD) and 25% of the aggregate economic 
burden is publicly funded (i.e., Medicaid, Medicare, and veterans' programs) (Florence et al. 
2016). 
In 2016, the Centers for Disease Control and Prevention (CDC) issued the “CDC Guideline for 
Prescribing Opioids for Chronic Pain” which encourages the use of alternatives to opioids and 
other practices that minimize harm to patients (Dowell et al. 2016). Despite the introduction of 
this guideline, primary care clinicians, who prescribe 45% of all opioid prescriptions in the US, 
report challenges in following these recommendations (Kroenke et al. 2019). The dynamics of 
opioid use make following guidelines difficult. Since opioid analgesia from a given dose declines 
with chronic use due to opioid tolerance, doses increase and the chance of harm grows. Over 
time, the primary benefit of opioids for many patients becomes the avoidance of withdrawal. As 
patients become dependent on opioids, they may misconstrue the treatment of interdose 
withdrawal hyperalgesia as ongoing effectiveness and they may become reluctant to stop opioids 

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(Juurlink 2017). More cautious opioid prescribing (including fewer new starts, avoidance of high 
doses, and slow, collaborative tapers for those already on high dose long-term therapy) can 
improve the balance of benefits and harms for patients with chronic pain. To embrace more 
cautious prescribing, a clinician may need to be informed that opioid risks are present and 
relevant to his/her own patients. 
2.2 Study Rationale 
Prescriber’s invited to join efforts to exercise more careful use of opioids may be more likely to 
do so after they have been made aware of an opioid death in their practice. They may also be 
more likely to dedicate themselves to amending their prescribing practices, committing to 
become an “aide in practice” and identify careful use of opioids with their self-image in the 
future. Many physicians underestimate the risk of opioids in their own practice, and currently, 
there is no mechanism for medical examiners to identify prescribing physicians, nor for 
physicians to be alerted about deaths, despite substantial interest from the CDC on solving this 
problem. One promising strategy for changing physician behavior is through use of “nudges”. 
The term “nudge” identifies a set of social and cognitive devices that persuade decisions in 
subtle ways while preserving choice. When prescribing narcotics, physicians may not have 
recent experiences that come to mind as to why these drugs are dangerous, making risks seem 
remote. They may also not consult data on a patient’s other narcotic prescriptions in their State’s 
pharmaceutical drug monitoring program. One useful nudge capitalizes on a finding called 
“Availability”.  Availability is the notion that people rely upon knowledge that is salient, recent 
and readily available to them to evaluate risks and make decisions. If a physician issued a 
prescription for a narcotic that resulted in a recent opioid poisoning, providing that physician 
with this feedback may make the physician more likely to consult a pharmaceutical drug 
monitoring program database and also prescribe more judiciously. 
3. STUDY DESIGN  
The trial is a cluster-randomized controlled trial with prescriber as the unit of randomization. The 
primary aim is to test the ability of the informative intervention to reduce the number of 
Scheduled drug prescriptions. We will randomize prescribers in a 1:1 design where half will be 
in the control group while the other half receives the intervention. All physicians involved in 
prescription drug over-dosage related deaths in the 12-month baseline period will be randomized 
to receive a letter regarding that death as well as guidelines for future consideration regarding 
prescribing practices. The intervention will be followed by a one year follow-up period to 
measure persistence of effects. Data from the Controlled Substance Utilization Review and 
Evaluation System 2.0, which is California’s prescription drug monitoring program, will be 
extracted and transferred to the Data Coordinating Center on a weekly basis.  
 

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4. SELECTION AND ENROLLMENT OF PARTICIPANTS  
4.1 Inclusion Criteria  
The subjects involved in this trial are clinicians will be from those who prescribe Scheduled 
drugs and practice in the County of San Diego. Clinicians must meet the following inclusion 
criteria to be selected to participate in the study: 1) have given at least one prescription for any 
sort of opioid medication to patients and 2) prescribed to a patient who have deceased within the 
12-month baseline period where prescription drug overdose was the primary cause or contributed 
to the cause of death. 
4.2 Exclusion Criteria  
Clinicians not meeting inclusion criteria above will be excluded from the study. 
4.3 Study Enrollment Procedures  
Participants will not be enrolled; we will intervene on all clinicians and allied health 
professionals with prescribing privileges and at least one prescription drug death as part of a 
public health program to encourage safe prescribing in San Diego County.  Informed consent of 
patients and clinicians is waived under HHS regulations at 45 CFR 46.116(c), as the study is 
evaluating a County public service safe prescribing program. Deaths where prescription drugs 
are the primary or contributing cause will be cataloged by the medical examiner between the 
period of July 1st, 2015 and June 30th, 2016. As part of the County safe prescribing program, 
prescribers to those deaths are identified in the CURES system. All California pharmacies and 
clinics that dispense controlled substances must submit reports to CURES on a weekly basis. In 
consultation with the program manager of CURES, a letter notifying CURES administrators that 
prescriptions from these clinicians would be evaluated prospectively after the safe prescribing 
letters were sent was submitted to the CURES system.  
5. STUDY INTERVENTIONS  
5.1 Interventions, Administration, and Duration  
A letter signed by the Chief Deputy Medical Examiner of San Diego County will notify 
prescribers of the death in their practice. The letter will outline the annual number and types of 
prescription drug deaths seen by the medical examiner, discussed the value of and way to access 
the State’s prescription drug monitoring program, and discussed five CDC guideline-
recommended safe prescribing strategies: 1) Avoid co-prescribing of opioids with 
benzodiazepines, 2) prescribe minimal dose necessary for acute pain, 3) consider slow tapers 
with pauses to below 50 MME per day, 4) avoid prescriptions lasting greater than 3-months for 

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pain, and 5) prescribe naloxone in conjunction with opioids for patients taking > 50 MME per 
day.  
5.2 Handling of Study Interventions  
Letters will be carefully edited by an advisory group to ensure tone is supportive and its 
recommendations consistent with CDC guidelines. Letters will be generated, signed by the San 
Diego Medical Examiner and posted in the U.S. mail.  The pre-intervention baseline period will 
be 12 months in length with a one year follow-up period to measure the persistence of effects 
after the intervention.  
5.3 Adherence Assessment  
Adherence is compulsory as prescription fills are automatically processed through pharmacies in 
the State and sent to the State’s prescription drug monitoring program. 
6. STUDY PROCEDURES   
6.1 Schedule of Evaluations 

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6.1.1 Screening Evaluation 
All clinicians whom meet the inclusion criteria will be eligible for the trial. Eligibility 
will be based on historical data from the CURES database.  
Consenting Procedure 
Not applicable.  We will be requesting a waiver of consent under HHS regulations at 45 
CFR 46.116(c). 
6.1.2 Enrollment, Baseline, and/or Randomization 
Enrollment 
Participants will not be enrolled; we will intervene on all clinicians and allied health 
professionals with prescribing privileges and at least one prescription drug death as part of a 
public health program to encourage safe prescribing in San Diego County.  Informed consent of 
patients and clinicians is waived under HHS regulations at 45 CFR 46.116(c), as the study is 
evaluating a County public service safe prescribing program.  
Baseline Assessments 
For the baseline assessment, the study team will evaluate physician prescribing practices in the 
12 months pre- intervention using data from the CURES database. 
Randomization 
For randomization, six decedent lists were generated from the crossed strata levels. Using 
random.org’s sequence generator,(16) true random integer sequences derived from atmospheric 
noise determined decedent order in each list. For each ordered list, prescribers to decedents in the 
first half were those who received the intervention.  
6.1.3 Follow-up Visits    
The follow-up observation period includes evaluating physician prescribing practices in the 13 
months following letter receipt. 

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6.1.4 Completion/Final Evaluation  
A participant is considered to have completed the study if he or she has completed the 
baseline assessment, received a letter, and been observed for >13 months following letter 
receipt. The end of the study is defined as completion of the >13-month data observation 
period following receipt of letter. 
7. SAFETY ASSESSMENTS  
7.1 Specification of Safety Parameters 
Although we will be unable to determine if there are hospitalizations or ED visits for opioid 
withdrawal in patients of clinicians in the intervention group, we will monitor this potential 
safety issue by comparing clinicians who received a letter to controls to determine if there are 
any dramatic reductions (>20% from baseline) in opioid prescribing in the post-intervention 
period as a result of the letter.  The U.S. does not have an integrated healthcare system with 
complete data on hospitalizations for all patients to evaluate induced withdrawal. However, it is 
possible for us to evaluate if providers induce withdrawal in patients by exhibiting high rates of 
MME reductions. 
7.2 Methods and Timing for Assessing, Recording, and Analyzing Safety Parameters 
We will assess safety throughout the course of the intervention period and follow-up period. 
7.3. Adverse Events and Serious Adverse Events  
At any time, clinicians can report an adverse event or unanticipated problem potentially related 
to the letters to the medical examiner’s office or the County or City public health officer who co-
signs the letter and provides contact information.  
We do not expect there to be adverse events directly influenced by the clinical guidance being 
delivered in this study. All study interventions encourage clinicians to follow well-established 
national guideline recommendations and known best practices. While the expectedness of 
adverse events is very low, we will investigate each and every numerator case identified in all 
safety measures described above. For cases identified by the safety monitoring measures, we will 
perform manual physician chart review to examine the clinical circumstances and to make a 
judgment (1) the expectedness of the event [unexpected, expected], (2) the likelihood that the 
safety event was study related [not related/possibly related/definitely related] and (3) judge the 
event’s severity [abnormal clinical finding without symptoms/symptoms requiring clinical 

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intervention/short term disability or hospitalization/death AND separately define the severity as 
mild, moderate, or severe].  
These will be conducted only by authorized study personnel. Study personnel will interview 
clinicians treating patients when needed to obtain additional information. Each case 
identified will have a case report form with these variables and will be signed and dated by 
study staff completing the form. These forms will be stored in a locked office. Each adverse 
event will be given an identification number. If study personnel believe that a patient that 
experienced an adverse event would benefit from seeing or communicating with their 
clinician who previously received a letter, the PI will within 2 business days reach out to this 
clinician advising them to contact the patient as soon as possible.  
7.4 Reporting Procedures 
The seriousness of the adverse event will be documented on the case report form. We will 
categorize all of the following as serious adverse events: patient death, life-threatening event, 
hospitalization-initial or prolonged, disability/incapacity, and events that required 
intervention to prevent permanent impairment. The clinician reviewing the event will 
determine the seriousness of the event. If it is an event other than those listed above that the 
reviewing clinician feels is an ‘other’ serious event, it will be discussed with another clinical 
study team member to reach consensus. All details will be documented on a case report form. 
We will within a business day report any clinician reported adverse events, safety analysis, or 
unanticipated problems to the USC IRB. Our report will include appropriate identifying 
information for the study, a detailed description of the adverse event, and a description of any 
changes to the protocol or other corrective actions that have been taken or are proposed. If an 
adverse event occurs, we will review relevant clinical decision support and ensure others are 
not at a greater risk of harm than was previously known or recognized. Clinician participants 
are only receiving a letter informing them of a recent patient death due to opioid overdose. 
Additional patient deaths related to this study are not expected. However, should we identify 
a patient death in safety measures described above we will report the death to the USC IRB 
within 24 hours of our knowledge of the death.  
7.5 Follow-up for Adverse Events 
We will within a business day report any clinician reported adverse events, safety analysis, or 
unanticipated problems to the USC IRB. Our report will include appropriate identifying 
information for the study, a detailed description of the adverse event, and a description of any 
changes to the protocol or other corrective actions that have been taken or are proposed. If an 
adverse event occurs, we will review relevant clinical decision support and ensure others are not 
at a greater risk of harm than was previously known or recognized. 

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7.6 Safety Monitoring  
Although we will be unable to determine if there are hospitalizations or ED visits for opioid 
withdrawal in patients of clinicians in the intervention group, we will monitor this potential 
safety issue by comparing clinicians who received a letter to controls to determine if there are 
any dramatic reductions (>20% from baseline) in opioid prescribing in the post-intervention 
period as a result of the letter.  The U.S. does not have an integrated healthcare system with 
complete data on hospitalizations for all patients to evaluate induced withdrawal. However, it is 
possible for us to evaluate if providers induce withdrawal on patients by exhibiting high rates of 
MME reductions. 
8. INTERVENTION DISCONTINUATION  
It is not possible to discontinue intervention because prescribers will receive one instance of the 
informative communication. In the secondary research design, prescribers will receive a follow 
up communication if the researchers find another case where an affiliated patient has deceased. 
9. STATISTICAL CONSIDERATIONS  
9.1 General Design Issues  
The study is a decedent-cluster randomized field experiment; clusters of prescribers within each 
decedent were randomly allocated to either treatment or control. This design avoided treatment 
contamination—clinicians prescribing to the same patient were able to share information about 
the intervention without diluting its effect. If two or more decedents had the same prescriber, that 
prescriber was assigned to one and only one of those decedents by random draw. Two factors 
served as random strata: 1) whether the decedent received a prescription from a clinician with a 
single or multiple deaths, and 2) whether opioids only, opioids in combination with 
benzodiazepines, or benzodiazepines only were the cause of death. The first strata is designed to 
form equivalent groups on prescriber risk posture. The second strata equated preference for type 
of scheduled drug prescribed in practice. Strata were crossed to form 2 x 3 = 6 randomization 
groups. 
9.2 Sample Size and Randomization 
We calculated the sample size needed to detect an effect of the letter intervention using standard 
formulas for power analysis in cluster randomized trials and the ‘clusterPower’ package in R 
statistical computing language.(19) For this calculation we used data on decedents and 
prescribers at the San Diego County Medical Examiner between January 1 and December 31, 
2013 published in Lev et al. 2016.(20) We assume a two-tailed test with a 5% Type I error rate 

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and an 80% chance to detect an effect. Lev et al.(20) found mean of 4.5 prescribers for each 
decedent in San Diego County. Coefficient of variation was not reported by Lev et al. 2016. We 
assume it to be 1.22, which implies that 99% of decedents had 20 or fewer prescribers in the year 
before their death. We assume a 5% difference in means and a standard deviation of +140 daily 
MME within cluster. Most clinician clusters for process measures have intracluster correlations 
that fall between 0.05 and 0.15.(21) We assume a more conservative intracluster correlation of 
0.2; in our design we randomized by decedent to reduce intracluster correlation. We utilized the 
Taylor method for calculating variance inflation due to unequal cluster sizes. Under these 
assumptions, we would need 65 decedents per study arm.   
9.2.1 Treatment Assignment Procedures. 
The study is a decedent-cluster randomized field experiment; clusters of prescribers within each 
decedent were randomly allocated to either treatment or control. This design avoided treatment 
contamination—clinicians prescribing to the same patient were able to share information about 
the intervention without diluting its effect. If two or more decedents had the same prescriber, that 
prescriber was assigned to one and only one of those decedents by random draw. Two factors 
serve as random strata: 1) whether the decedent received a prescription from a clinician with a 
single or multiple deaths, and 2) whether opioids only, opioids in combination with 
benzodiazepines, or benzodiazepines only were the cause of death. The first strata is designed to 
form equivalent groups on prescriber risk posture. The second strata equated preference for type 
of scheduled drug prescribed in practice. Strata were crossed to form 2 x 3 = 6 randomization 
groups. 
For randomization, six decedent lists were generated from the crossed strata levels. Using 
random.org’s sequence generator, true random integer sequences derived from atmospheric noise 
determined decedent order in each list. For each ordered list, prescribers to decedents in the first 
half were those who received the intervention. 
9.3 Interim analyses and Stopping Rules 
Not applicable 

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9.4 Outcomes  
9.4.1 Primary outcome   
Our primary analysis assesses changes over time before and after the intervention in natural 
log-transformed MME between treatment and control.  
9.4.2 Secondary outcomes   
As secondary hypotheses, we evaluate if the intervention cohort had 1) fewer high-dose 
opioid prescriptions (≥ 50 or > 90 MME per day), or 2) fewer new patients who were started 
on opioids. High-dose prescriptions were evaluated using censored regression on percent of 
clinician fills on high-dose. Chance of a fill being a “new start” were evaluated using logistic 
regression. These analyses were also conducted within a difference-in-differences 
framework. 
9.5 Data Analyses  
Descriptive and inferential statistics will be carried out in STATA (Version 14.0; Stata 
Corporation, College Station, TX). The cmp command in STATA will be used to compute a 
difference-in-differences estimator within a mixed-model censored linear regression.(17) The 
difference-in-differences estimator compares the average change over time in MME dispensed 
for prescribers in the intervention group, compared to the average change over time for 
prescribers in the control group. The natural log transformation of MME ensures normally 
distributed data. Censored regression has a continuous component and a discrete one. Natural log 
MME doses estimated over days where opioids were dispensed in the name of a prescriber 
represent the continuous part, and days with no opioids filled in that prescriber’s name represent 
the discrete part of the model. We denote estimation of the dependent variable as 
 
to distinguish it from uncensored estimation. The analysis is represented by: 
       [1] 
where 
,
and
 are fixed effects coefficients on time, 
, intervention, 
,and time by 
intervention interaction, 
, respectively. Alphabetic subscripts describe the  ith prescriber, jth 
prescription filled and kth decedent, the nested random intercept 
is normally distributed with 
mean zero and variance, 
, for each i prescriber nested in (i.e., having prescribed to) decedent 
k, i(k). With natural log transformed data, the value 100·[exp{
} - 1] measures the percentage 
change in MME attributable to the intervention when data are uncensored.(18) However, when 
data are censored the coefficient 
 must be adjusted to reflect the effect on the observed 
outcome. We will evaluate data 3 months pre-intervention and 3 months post-intervention, with 

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post-intervention beginning one month after the letter was sent to washout contamination by 
prescriptions written before receipt of the letter but filled afterwards. For the one-year analysis 
we will include a regressor to control for the 3-month effect. We will perform sensitivity analysis 
on regression assumptions. As secondary hypotheses, we evaluate if the intervention cohort had 
1) fewer high-dose opioid prescriptions (≥ 50 or > 90 MME per day), or 2) fewer new patients 
who were started on opioids. High-dose prescriptions will be evaluated using censored 
regression on percent of clinician fills on high-dose. Chance of a fill being a “new start” will be 
evaluated using logistic regression. These analyses will also be conducted within a difference-in-
differences framework. 
10. DATA COLLECTIO N AND QUALITY ASSURANCE 
10.1 Data Collection Forms  
The Controlled Substance Utilization Review and Evaluation System (CURES) will 
provide records of opioids dispensed at California pharmacies attributable to each 
provider in our sample treating all civilian, non-Veteran’s Administration and non-
institutionalized patients. CURES is California’s prescription drug monitoring program. 
The Medical Examiner has authority to use CURES for the purpose of educating 
practitioners and others in lieu of disciplinary, civil, or criminal actions, in accordance 
with the California State’s Health and Safety Code § 11165(c)(2). First name, last name, 
date of birth and address identified each decedent in the medical examiner reports and 
CURES data. Drug Enforcement Agency (DEA) number identified the prescribers in 
CURES data. Data from eligible clinicians will be extracted from CURES and kept onsite 
at the medical examiner’s office for de-identified analytic file preparation. A file stripped 
of patient and clinician identifiers will be prepared and released for analysis on secure 
servers at the University of Southern California. 
10.2 Data Management  
Each of the participating sites will create an extract from the CURES database for all 
deceased patients where prescription drug over-dosage was the primary cause or 
contributed to the cause of death. These records will be accessed, extracted, then 
transferred to the localized database in the coordinating center on a weekly basis.  The 
coordinating center has created programs and quality control queries for transforming all 
of the data into a standard model (Observational Medical Outcomes Partnership Common 
Data Model, version 3). The data collection forms will be CURES database extracts.  

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10.3 Quality Assurance  
10.3.1 Training 
All project personnel handling study data will be certified by the Collaborative IRB 
Training Initiative (CITI) program, which consists of courses in the Protection of Human 
Research Subjects for Biomedical Research. Staff will trained on the permissible values 
present in the CURES database, frequency of update, and expected volumes of data. 
10.3.2  Quality Control Committee  
The quality control committee consisted of practicing clinicians from each participating 
clinical organization. They reviewed automatically refreshing dashboards for potential 
deviations in coding systems and appropriate values for codes included in the outcome 
measures. These dashboards were reviewed prior to each email distribution. 
10.3.3  Metrics 
Quality control metrics were based on reports were based on verifications for prescription 
with National Drug Codes for Schedule II/III/IV drugs following the FDA Drug 
Schedule.  
10.3.4 Protocol Deviations 
Our task tracking system, JIRA was used to track and document issues. Each issue 
included an assignee and a reviewer. 
10.3.5 Monitoring 
In addition to data quality reviews, we will also review the integrity of the interventions. It is the 
primary role of the data analysts to: 1) determine the appropriate number of eligible providers to 
reach sufficient statistical power, 2) record provider information in a secure, password protected 
database located at LA County Medical Examiner-Coroner’s office and, 3) track enrolled 
providers in regards to their intervention status and progress.  On an approximately quarterly 
basis, staff would visit headquarters of participating sites and verify functionality of decision 
support tools. Additionally, practicing clinicians on our study team had the ability to monitor 
electronic medical record interventions in their own health systems.     

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11. PARTICIPANT RIGHTS AND CONFIDENTIALITY  
11.1 Institutional Review Board (IRB) Review  
The study protocol will be reviewed and approved by the University of Southern California’s 
Institutional Review Board (IRB). Individual site protocols will also be submitted for review and 
approval by the California site local IRB. 
11.2 Informed Consent Forms 
We will request a waiver of consent. 
11.3 Participant Confidentiality  
Data will be recorded on a localized data warehouse. Data will be kept in encrypted files on 
computers in locked offices at San Diego County Medical Examiner’s Office facilities. Only 
study investigators will have password protected access to a restricted patient and prescriber 
information.  
11.4 Study Discontinuation  
Following each DSMB meeting, the board will make recommendations to the local IRBs as to 
whether the study should continue or if changes to the protocol are necessary for continuation. 
12. COMMITTEES 
Not applicable 
13. PUBLICATION OF RESEARCH FINDINGS  
Publication of results from our research will follow the NIH Public Access Policy, which 
requires that we submit to the National Library of Medicine’s PubMed Central an electronic 
version of final, peer-reviewed manuscripts upon acceptance for publication, to be made publicly 
available no later than 12 months after the official date of publication. 
 

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14. REFERENCES   
D. Boudreau et al., “Trends in long-term opioid therapy for chronic non-cancer pain,” 
Pharmacoepidemiol. Drug Saf., vol. 18, no. 12, pp. 1166–1175, 2009. 
D. Dowell, T. M. Haegerich, and R. Chou, “CDC Guideline for Prescribing Opioids for Chronic Pain 
- United States, 2016,” MMWR Recomm. Rep., vol. 65, no. 1, pp. 1–49, Mar. 2016. 
C. S. Florence, C. Zhou, F. Luo, and L. Xu, “The Economic Burden of Prescription Opioid Overdose, 
Abuse, and Dependence in the United States, 2013,” Med. Care, vol. 54, no. 10, pp. 901–906, 
Oct. 2016. 
G. P. Guy Jr et al., “Vital Signs: Changes in Opioid Prescribing in the United States, 2006-2015,” 
MMWR Morb. Mortal. Wkly. Rep., vol. 66, no. 26, pp. 697–704, Jul. 2017. 
D. N. Juurlink, “Rethinking ‘doing well’ on chronic opioid therapy,” CMAJ, vol. 189, no. 39, pp. 
E1222–E1223, Oct. 2017. 
E. E. Krebs et al., “Effect of Opioid vs Nonopioid Medications on Pain-Related Function in Patients 
With Chronic Back Pain or Hip or Knee Osteoarthritis Pain: The SPACE Randomized Clinical 
Trial,” JAMA, vol. 319, no. 9, pp. 872–882, Mar. 2018. 
K. Kroenke et al., “Challenges with Implementing the Centers for Disease Control and Prevention 
Opioid Guideline: A Consensus Panel Report,” Pain Med., vol. 20, no. 4, pp. 724–735, Apr. 
2019. 
L. Scholl, “Drug and Opioid-Involved Overdose Deaths — United States, 2013–2017,” MMWR 
Morb. Mortal. Wkly. Rep., vol. 67, 2019, doi: 10.15585/mmwr.mm6751521e1. 
15. SUPPLEMENTS/APPENDICES 
OPIOIDS LIST 
Included 
Excluded 
Codeine 
Buprenorphine 
Fentanyl tablet 
Butorphanol 

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Fentanyl patch 
Dihydrocodeine 
Hydrocodone 
Fentanyl lozenge 
Hydromorphone 
Fentanyl powder* 
Methadone 
Fentanyl spray* 
Morphine 
Levorphanol tartrate 
Oxycodone 
Meperidine 
Oxymorphone 
Opium 
Tapentadol 
Tramadol 
*Excluded because prescription was likely administered in office by a healthcare professional 
List taken from CDC Commonly Prescribed Opioids 
https://www.cdc.gov/mmwr/volumes/65/rr/rr6501e1.htm 

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