TITLE: Reducing opioid prescribing in a primary care resident clinic: a practical approach.

Authors:José Luis González, MD1,2,3

Radhika Prabhakar, MD1,2

Jennifer Marks, MD1,2

Cheryl L.P. Vigen, PhD1,4,5

Jagruti Shukla, MD2

Beatrisa Bannister, PharmD2

1 University of Southern California, Los Angeles, CA2 LAC+USC Medical Center, Los Angeles, CA3 USC Gehr Family Center for Health Systems Science4 Biostatistics, Epidemiology, and Research Design (BERD)5 Southern California Clinical and Translational Science Institute (SC CTSI)

Corresponding Author:José Luis González, MD 2020 Zonal AvenueIRD 306Los Angeles, CA 90033gonza40@usc.edu424-835-1406

Abstract word count: 247

Body word count: 2,598

To the best of our knowledge, no conflict or competing interest, financial or other, exist.

Funding for the statistical analysis of the data was provided by the Division of Geriatric, Hospital, Palliative and General Internal Medicine at the University of Southern California.

1

ABSTRACT

Background: Reducing high-dose opioid prescribing is a uniquely challenging endeavor in

resident primary care clinics. Few studies describe successful opioid tapering strategies in this

setting.

Objectives: To describe the efficacy of a comprehensive approach aimed at reducing opioid

prescribing in an Internal Medicine resident clinic.

Methods: In this retrospective observational study, we reviewed pharmacy dispensing data for

two hospital-affiliated pharmacies for resident primary care patients filling opioid prescriptions

between July 2016 and July 2018. We instituted a comprehensive set of interventions that

included resident education, limiting supervision of encounters for long-term opioid therapy

(LTOT) to a fixed set of faculty champions, and providing alternate modalities for pain control.

We calculated the change in number of opioid prescriptions dispensed, number of patients

receiving opioid prescriptions, morphine milliequivalents (MMEs) dispensed, and average per-

patient daily MMEs dispensed.

Results: We observed an average monthly reduction of 2.44% (p<0.001) in the number of

prescriptions dispensed and a 1.83% (p<0.001) monthly reduction in the number of patients

receiving prescriptions. Over the two-year period, there was a 74.3% reduction in total MMEs

prescribed and a 66.5% reduction in the average MMEs prescribed per patient.

Conclusions: Our findings demonstrate a significant reduction in opioid prescribing after

implementation of a comprehensive initiative. Although our study was observational in nature,

we observed a nearly three-fold decrease in opioid prescribing compared to national trends. Our

2

results offer important insights for other primary care resident clinics hoping to engender safe

prescribing practices to trainees and curb high-dose opioid prescribing.

3

INTRODUCTION

Over the last two decades, opioid-related deaths have increased gradually and then since

2014, precipitously.1 Opioids are now the leading cause of death for individuals under the age of

55, accounting for a quarter of all deaths for those between 25 and 54.2 The impact is so extreme,

that life expectancy in the U.S. has now declined for three consecutive years, driven mainly by

suicide rates and opioid abuse.3 Opioid-related deaths accounted for 13.3 deaths per 100,000

individuals in 2016, or put another way, for 2.3% of all deaths that year, overtaking the 8th

leading cause – pneumonias and influenza.

The role of primary care providers in the opioid epidemic is substantial– Family

Medicine, Internal Medicine and General Practice account for nearly half of all prescribed

opioids.4 In 2016, the Center for Disease Control and Prevention (CDC) released guidelines for

the safe prescribing of opioids for chronic non-cancer pain (CNCP).5 These guidelines emphasize

careful consideration prior to initiating treatment with opioids and recommend tapering for

patients who do not benefit from high doses, yet the practical application of the guidelines

remains a challenge.

Evidence regarding opioid tapering strategies is limited to small, poor-quality studies.5 In

one observational study, investigators detailed their approach across an entire health system,6 and

only one other study details successful interventions in a resident training clinic.7

Curtailing opioid prescribing for patients with CNCP in academic resident clinics poses

special challenges. Resident physicians consistently cite a lack of training, knowledge and

comfort level in managing patients with chronic pain.8-10 One study concluded that residents

routinely interpret urine drug screens inaccurately.11 Residents, as compared to attending

4

physicians, are more likely to prescribe opioids for longer, provide more early refills, continue

prescribing opioids to patients who are receiving them from multiple providers and be told by

patients that prescriptions were lost or stolen.12,13 It is clear that a large knowledge gap and

equally substantial educational opportunity exist.

Furthermore, lack of provider-patient continuity is a well-documented predicament in

resident teaching clinics,14,15 and residents often cite lack of familiarity with a patient’s

longitudinal care as a reason for not tapering high-dose opioid regimens. Multiple studies have

found an inverse correlation between provider-patient continuity and risky opioid prescribing

practices.,16 Continuity of care remains a relatively unstudied variable of interest in reducing

opioid prescribing.

LAC+USC Medical Center is a large, urban safety-net health center with two sister

clinics – a direct patient care clinic and a resident clinic. A significantly higher proportion of

patients in the resident clinic were receiving opioids for the treatment of CNCP despite both

clinics having access to the same resources and serving patients of the same demographic. We

implemented a set of local interventions with the intent of curbing high-dose opioid prescribing

in the resident clinic. The purpose of this retrospective study is to assess the impact of a

comprehensive approach on opioid prescribing for CNCP in a resident primary care clinic.

METHODS

Patient Selection and Data Source:

Pharmacy dispensing data for the Internal Medicine resident clinic was collected and de-

identified for patients filling their prescriptions at one of two LAC+USC pharmacies between

July 2016 and June 2018. Approximately 75% of all patients seen at this clinic opt to fill their

5

prescriptions at these pharmacies. Prescriptions written by providers outside the LAC+USC

institution are not honored. Prescriptions were written by one of approximately 165 Internal

Medicine residents working in the clinic, or, for unlicensed residents, by the supervising

attending physician. Data collected included drug name, quantity, frequency, dose dispensed and

prescriber. Dispensed opioids were used as a proxy for prescribed opioids. This study was

approved by the University of Southern California Institutional Review Board.

Only schedule II opioids were included. Customarily, the clinic does not use methadone

for managing CNCP and no patients were prescribed methadone from our clinic during the

course of the study. All schedule II opioids were included irrespective of whether patients

presented for a one-time fill or a refill. Although patients with cancer or other life-limiting

illnesses are seen in our clinic, these patients typically receive prescriptions for pain management

from their oncology providers and thus are not included in these data.

Interventions:

We relied on several county-wide services established prior to the start of this study. The

Wellness Center, opened in 2014, is a walk-in facility adjacent to the hospital that offers free-of-

charge non-pharmacologic pain management services including yoga, acupuncture, exercise and

mental well-being classes.

Multiple interventions focused on the resident clinic were instituted during the study

period. Residents received three sets of lectures, with topics relating to the non-pharmacologic

management of CNCP, safe opioid prescribing practices, and the identification and treatment of

opioid use disorder. In addition, a grand rounds and several small group discussions took place.

Second, we made efforts to ensure that patients received follow-up with a consistent resident

6

provider. We also instituted a policy requiring all patients receiving long-acting / extended

release (LA/ER) formulations for long-term opioid therapy (LTOT) to have mandated monthly

visits. We felt that this policy would capture patients receiving the highest doses and thus at

highest risk for adverse events. The supervising attending physician was required to guide the

conversation in cases where an escalation or initiation of opioid therapy was being considered.

Other interventions included screening for anxiety and depression by nursing staff and the

integration of psychiatry and social work services into the clinic. A comprehensive list of

interventions is provided in Table 1.

Initially, the responsibility of staffing the resident clinic was evenly divided between

twenty-nine faculty members, but beginning in July 2016, three “core” faculty were assigned to

70% of clinic sessions. In February, 2017, we instituted a policy that only the three core faculty

were allowed to supervise encounters with patients receiving LTOT. The core faculty members

adhered closely to the CDC guidelines, which entailed focusing on patients receiving high doses

without an improvement in pain or function, evidence that the risks of opioid therapy outweighed

its benefits or evidence of surreptitious drug-seeking behavior.5 Tapering schedules were

individualized and abrupt cessation was avoided.

Study Cohorts, Outcomes of Interest and Data Analysis:

We recorded the number of prescriptions for opioids sent from the primary care clinic

and filled at one of two onsite pharmacies. We performed a subgroup analyses of LA/ER and of

immediate-release (IR) formulations. A by patient analysis was also performed. However, to

avoid counting patients twice, those receiving both were counted under the LA/ER category as

this group included patients receiving relatively higher doses. Data were analyzed using negative

7

binomial regression analysis to model the relationship between time (months as measured from

July 2016) and the number of opioid prescriptions or number of patients receiving prescriptions.

We were also interested in the quantity of opioids prescribed. Thus, prescribed opioids

were converted to standardized MMEs (morphine milligram equivalents) consistent with

published CDC reference tables of equivalencies.17 The number of MMEs dispensed were plotted

over the 24-month study period and statistical significance was assessed via ordinary linear

regression analysis.

Several studies describe a linear relationship between daily MME intake and mortality

risk, starting with a daily doses as low as 50 MMEs whereas the CDC guidelines recommend

justification for patients receiving doses in excess of 90 MMEs.5,18,19 Thus, we also calculated

how many patients received opioids above certain thresholds (50, 100 and 150MMEs) to

visualize trends for each of these high-risk categories. Finally, we calculated and plotted the

average daily MMEs prescribed to patients over the 2-year study period. Statistical significance

was calculated using linear regression analysis.

RESULTS

Between July 2016 and June 2018, a total of 1360 prescriptions originating from the clinic

were dispensed on 933 separate occasions from LAC+USC pharmacies. We observed an average

monthly decrease of 2.44% (p = <0.001) prescriptions. Restricting prescriptions to LA/ER

formulations only, revealed an even greater monthly decline of 3.70% (p = <0.001). A more

modest monthly reduction in IR formulations of 1.64% (p=0.001) remained significant. See

Table 2 for estimated values and Table S2 in the supplemental appendix for actual values.

8

Calculations by patient also showed a significant decline in number of patients receiving

opioids. Subgroup analysis of LA/ER formulations and IR formulations mirrored the by

prescription results discussed above, demonstrating a 1.83% (p<0.001) monthly decline in total

and 3.54% (p<0.001) monthly decrease in LA/ER formulations (Table 2). We observed a slight

increase in the number of patients receiving IR formulations, although this change was not

significant. Figure 1 depicts the number of patients receiving opioids.

At the beginning of the study, 19, 10 and 5 patients received more than 50, 100 and 150 daily

MMEs respectively. We observed an average monthly decrease of 4.75% (p<0.001), 5.37%

(p<0.001) and 6.76% (p<0.001) in each of these subgroups respectively (Figure 1).

In July 2016, 90,525 MMEs were dispensed with an average monthly decrease of 2898

(5.04%) MMEs (p<0.001) as calculated by linear regression analysis. After two years, this

number had decreased to only 23,230 MMEs, which represents an overall decrease of 74.3%.

The LA/ER subgroup also reached statistical significance, with an average monthly decrease of

2075 (5.50%) MMEs (p<0.001). The same is true for MMEs of patients receiving only IR

formulations with an average monthly decrease of 823 (4.20%) MME (p=0.001) (Figure 2).

Finally, the average daily MMEs were calculated for patients based on the overall

monthly total MMEs and the number of patients who received opioids that month (Figure 3). The

average prescribed MMEs decreased from 67.91 to 22.77 MMEs per day which represents an

overall decrease of 66.5%.

DISCUSSION

9

In this observational study, we retrospectively reviewed pharmacy dispensing data in a

primary care resident clinic over a two-year period. During this time, several system-wide as

well as clinic-specific initiatives aimed at curtailing high-dose opioid prescribing were instituted.

We observed a significant decrease in the number of opioid prescriptions dispensed, quantity of

opioids dispensed and average quantity of opioids dispensed per patient. This occurred with only

a relatively minor decrease in the number of patients receiving opioids.

Our data revealed an increase in the absolute number of patients receiving IR

formulations. Typically IR formulations are more conducive to abuse than LA/ER

formulations.20 Furthermore, although no studies have established proof, it is thought that for a

given MME, IR formulations are more likely to result in overdose.5 This observation likely

represents a shift in prescribing from LA/ER formulations (i.e. high dose) to IR-only

formulations (lower dose) on the path of being weaned from high dose opioids. That the total

MMEs prescribed decreased over the course of the study supports this explanation.

While total dose of MMEs dispensed during the study showed a 74.3% decline across all

types of prescriptions (Figure 2), the number of patients who were dispensed opioids only

decreased by 21% (Figure 1: Total graph). These data suggest that few patients chose to leave

our clinic despite a reduction in their prescribed opioids.

Hard limits to opioid prescribing are considered inappropriate by experts and patients

alike21. Rather, they should be applied with flexibility and patient-specific factors in mind.

Nevertheless, multiple studies cite 50 MMEs as an important threshold at which patients are at

increased risk for opioid-related death.18,19 Initially, the average patient receiving opioids from

our clinic was prescribed opioids in excess of this dose - 67.91 MMEs per day. This number was

reduced to 22.77 by the end of the study. Although many patients continued to receive quantities

10

in excess of this average, current data suggest that this reduction has contributed to curtailed

morbidity and mortality in our clinic population.

The study spanned and coincided with two academic years in order to mitigate the effect

that resident training may have had at the beginning versus the end of the year. We hoped that

increasing resident-patient continuity would help us reduce opioid prescribing. This proved to be

an elusive ambition however, as requiring monthly visits for patients receiving LA/ER opioids

had the unintended consequence of hampering our efforts to consistently pair a patient with the

same resident provider. In these instances, the three core faculty members personally evaluated

each patient and served as the constant in the patient-provider relationship which was felt to be

fundamental to maintaining a therapeutic relationship, retaining patients, and managing their

pain. This staffing paradigm might be replicated by other training programs seeking to reduce

opioid prescribing in resident clinics.

As national opioid prescribing rates continue to decrease, some experts warn of

overreaction to the CDC prescribing guidelines.21-23 Faculty were careful to individualize

tapering strategies and avoided abrupt opioid discontinuation. Whereas the guidelines describe

tapering strategies involving reductions as high as 10-50% per week5, we found a more gradual

curtailment to be effective and more tolerable.

We were able to achieve this reduction in opioid prescribing while taking care to mitigate

withdrawal-related pain, in large part due to the increase in available resources listed in Table 1.

The availability of alternate modalities for pain control offered by the Wellness Center, the

identification and treatment of comorbid psychiatric conditions which are known to worsen

perception of pain,24-29 as well as the services afforded by pharmacists, medical assistants and

social work bolstered our efforts. Future studies are needed to explore the direct effect of patient-

11

provider continuity on opioid prescribing practices and whether continuity can be assumed by

supervising attending staff. In addition, the role of modeled attending-patient interactions for

negotiating mutually satisfactory treatment plans warrants further exploration.

Our study had several limitations. It was not within the scope of our study to use the

direct patient care clinic for comparison and therefore, we could not include a control group.

Second, as various interventions were already underway prior to the start of our study, we could

not employ a prospective design and instead relied on a retrospective design. Furthermore,

specific interventions were implemented at staggered intervals, which precludes us from

determining which were most pivotal in reducing opioid prescribing. Finally, national opioid

prescribing trends exhibited a downwards slope during our study and this background effect

should not be discounted. However, the national rate of decline in number of prescriptions

between 2016 and 2017 was 8.2%.22,23 This accounts for only a portion of the 22.2% yearly

decline in number of prescriptions seen in our cohort.

Our study was limited to pharmacy dispensing data at two on-site pharmacies. At the start

of the study, our medical center did not utilize electronic prescribing of controlled substances;

thus we were unable to collect information on prescriptions sent to outside pharmacies which

may have introduced sampling bias. It also seems likely that patients who were dissatisfied with

a reduction in their opioid dosage would seek other prescribers. However, the drastic reduction in

in quantity of opioids prescribed is not accounted for by the small number of patients who

stopped receiving opioids altogether. Finally, reporting on pain scales was not within the scope

of our study and it is possible that despite offering alternate pain management modalities our

patients’ levels of pain control were adversely affected.

12

CONCLUSION

Reducing high-dose opioid prescribing poses unique challenges in a primary care resident

clinic and few studies explore successful interventions. Following implementation of a

comprehensive approach for addressing opioid overuse, we observed a substantial reduction in

opioid prescribing by a variety of measures.

The observational nature of this study makes it unsuitable for causal inferences.

Nevertheless, our study offers important insights as to how residency clinics may meaningfully

reduce opioid prescribing. In addition to resident education and the increased use of non-

pharmacologic interventions for treating CNCP, resident clinics are encouraged to identify a set

of faculty members to oversee all patients receiving prescriptions for LTOT. This combination of

strategies may lead to a synergistic effect on curbing opioid prescribing.

ACKNOWLEDGEMENTS

Contributors: The authors would like to thank Drs. Michael Hochman and Michael Wang for

their guidance, Drs. Josh Banerjee, Barbara Rubino and Joanne Suh for their project support,

and Dr. Michael Karp for his administrative support.

There are no prior poster or abstract presentations, online publications or preprint publications

of this manuscript or the study described herein.

13

REFERENCES

1. Hedegaard H MA, Warner M. Drug overdose deaths in the United States, 1999-2017. NCHS Data Brief. 2018;329.

2. Centers for Disease Control and Prevention NCfHS. Multiple Cause of Death 1999-2017 on CDC WONDER Online Database, released December, 2018. Data are from the Multiple Cause of Death Files, 1999-2017, as compiled from data provided by the 57 vital statisics jurisdictions throuth the Vital statistics Cooperative Program. 2018.

3. Murphy S, Xu J, Kochanek KD, Arias E. Mortality in the United States, 2017. NCHS Data Brief. 2018(293):1-8.

4. Levy B, Paulozzi L, Mack KA, Jones CM. Trends in Opioid Analgesic-Prescribing Rates by Specialty, U.S., 2007-2012. Am J Prev Med. 2015;49(3):409-413.

5. Dowell D, Haegerich TM, Chou R. CDC Guideline for Prescribing Opioids for Chronic Pain--United States, 2016. Jama. 2016;315(15):1624-1645.

6. Losby JL, Hyatt JD, Kanter MH, Baldwin G, Matsuoka D. Safer and more appropriate opioid prescribing: a large healthcare system's comprehensive approach. J Eval Clin Pract. 2017;23(6):1173-1179.

7. Austin RC, Fusco CW, Fagan EB, et al. Teaching Opioid Tapering Through Guided Instruction. Fam Med. 2019;51(5):434-437.

8. Regunath H, Cochran K, Cornell K, et al. Is It Painful to Manage Chronic Pain? A Cross-Sectional Study of Physicians In-Training in a University Program. Mo Med. 2016;113(1):72-78.

9. Yanni LM, Weaver MF, Johnson BA, Morgan LA, Harrington SE, Ketchum JM. Management of chronic nonmalignant pain: a needs assessment in an internal medicine resident continuity clinic. J Opioid Manag. 2008;4(4):201-211.

10. Kavukcu E, Akdeniz M, Avci HH, Altug M, Oner M. Chronic noncancer pain management in primary care: family medicine physicians' risk assessment of opioid misuse. Postgrad Med. 2015;127(1):22-26.

11. Starrels JL, Fox AD, Kunins HV, Cunningham CO. They don't know what they don't know: internal medicine residents' knowledge and confidence in urine drug test interpretation for patients with chronic pain. J Gen Intern Med. 2012;27(11):1521-1527.

12. Colburn JL, Jasinski DR, Rastegar DA. Long-term opioid therapy, aberrant behaviors, and substance misuse: comparison of patients treated by resident and attending physicians in a general medical clinic. J Opioid Manag. 2012;8(3):153-160.

13. Khalid L, Liebschutz JM, Xuan Z, et al. Adherence to prescription opioid monitoring guidelines among residents and attending physicians in the primary care setting. Pain Med. 2015;16(3):480-487.

14. Walker J, Payne B, Clemans-Taylor BL, Snyder ED. Continuity of Care in Resident Outpatient Clinics: A Scoping Review of the Literature. J Grad Med Educ. 2018;10(1):16-25.

15. Butler M, Kim H, Sansone R. Improved continuity of care in a resident clinic. Clin Teach. 2017;14(1):45-48.

16. Hallvik SE, Geissert P, Wakeland W, et al. Opioid-Prescribing Continuity and Risky Opioid Prescriptions. Ann Fam Med. 2018;16(5):440-442.

14

17. Center for Disease Control and Prevention. Calculating Total Daily Dose of Opioids for Safer Dosage. https://www.cdc.gov/drugoverdose/pdf/calculating_total_daily_dose-a.pdf. Accessed January 13th, 2019.

18. Dunn KM, Saunders KW, Rutter CM, et al. Opioid prescriptions for chronic pain and overdose: a cohort study. Ann Intern Med. 2010;152(2):85-92.

19. Gomes T, Mamdani MM, Dhalla IA, Paterson JM, Juurlink DN. Opioid dose and drug-related mortality in patients with nonmalignant pain. Arch Intern Med. 2011;171(7):686-691.

20. Cicero TJ, Ellis MS, Kasper ZA. Relative preferences in the abuse of immediate-release versus extended-release opioids in a sample of treatment-seeking opioid abusers. Pharmacoepidemiol Drug Saf. 2017;26(1):56-62.

21. Dowell D, Haegerich T, Chou R. No Shortcuts to Safer Opioid Prescribing. N Engl J Med. 2019.22. Centers for Disease Control and Prevention. 2018 Annual Surveillance Report of Drug-Related

Risks and Outcomes - United States. Surveillance Special Report 2018.23. Centers for Disease Control and Prevention. Annual Surveillance Report of Drug-Related Risks

and Outcomes - United States. Surveillance Special Report 1. 2017.24. Arteta J, Cobos B, Hu Y, Jordan K, Howard K. Evaluation of How Depression and Anxiety Mediate

the Relationship Between Pain Catastrophizing and Prescription Opioid Misuse in a Chronic Pain Population. Pain Med. 2016;17(2):295-303.

25. Kivrak Y, Kose-Ozlece H, Ustundag MF, Asoglu M. Pain perception: predictive value of sex, depression, anxiety, somatosensory amplification, obesity, and age. Neuropsychiatr Dis Treat. 2016;12:1913-1918.

26. Magnussen H, Disse B, Rodriguez-Roisin R, et al. Withdrawal of inhaled glucocorticoids and exacerbations of COPD. N Engl J Med. 2014;371(14):1285-1294.

27. Ciaramella A. Mood Spectrum Disorders and Perception of Pain. Psychiatr Q. 2017;88(4):687-700.

28. Hermesdorf M, Berger K, Baune BT, Wellmann J, Ruscheweyh R, Wersching H. Pain Sensitivity in Patients With Major Depression: Differential Effect of Pain Sensitivity Measures, Somatic Cofactors, and Disease Characteristics. J Pain. 2016;17(5):606-616.

29. Woo AK. Depression and Anxiety in Pain. Rev Pain. 2010;4(1):8-12.

15

Table 1: Timeline of Interventions.

Year Month InterventionPre-start Opioid prescribing limits in place*

Wellness Center opensPharmacy oversight of patients receiving opioidsCounty-wide pain management workgroup formedOnline advice portal for providers managing patients with chronic painCDC guidelines for prescribing opioids for chronic pain published

2016 July Mandatory CURES database registration for all DEA-licensed providersSupervising responsibilities shifted to “core” faculty and limited pool of other facultyX-waivered provider available to provide medication-assisted treatment in clinic

Oct Resident Lecture: Safe Prescribing of OpioidsIntegration of social work into the medical home

Nov DHS (Department of Health Services) Opioid taper best-practice document published2017 Feb Implementation of policy limiting supervision of opioid prescribing to only 3 “core” faculty

Resident Lecture: Chronic Pain ManagementUniversal screening for depression and anxiety by nursing staff upon intakePsychiatry services integrated into the medical home

Mar Nursing staff automatically provides CURES report for providerMandated monthly visits for all patients receiving long-acting/extended release (LA/ER) LTOT

Aug Diclofenac gel added to pharmacy formulary2018 Mar DHS Controlled Substance Agreement revised

Apr Mandated electronic prescribing of controlled substances* Opioid prescribing limits are county-wide and were in place prior to the start of this study. The particular limits were chosen to standardize prescribing limits set forth by the major insurers in Los Angeles County including MediCAL, Health Net and LA Care. Requests for exceptions to these limits are available by filling out a non-formulary request form. Prescribing limits are provided in supplemental appendix S1.

Interventions are written in order of initiation. Interventions with a grey background are national, state, or county-wide and were not specifically a part of this initiative but likely had an effect on opioid prescribing. Interventions appearing in a white background are local, clinic-level interventions.

16

Table 2: Statistical Analysis: Estimated Change in Opioid Prescribing

By Prescriptions*Estimated number of opioid

prescriptions/month at beginning of study%Decrease per month in number

of prescriptions sentp-value

Total 76.04 2.44% <0.001 LA/ER 34.21 3.70% <0.001 IR 42.24 1.64% 0.001

By Patients*Estimated number of patients receiving

prescriptions/month at beginning of study%Decrease per month in number

of patients receiving opioidsTotal 48.62 1.83% <0.001 LA/ER (+/- IR) 25.23 3.54% <0.001 IR Only 23.94 0.58% NSMME Subgroups >50 20.41 4.75% <0.001 >100 10.12 5.37% <0.001 >150 7.57 6.76% <0.001

By MMEs †Intercept (std err) MMEs

prescribed/month at beginning of studyDecrease (std err) per month in

MMEs/month prescribedTotal 95785 (5263) 2898 (368) <0.001 LA/ER 65587 (3279) 2075 (229) <0.001 IR Only 30197 (2496) 823 (175) 0.001Avg Daily MME/Pt 68.43 (4.36) 1.51(0.31) <0.001

*The By Prescriptions and By Patients model use number of prescriptions and patients, respectively, per month analyzed using negative binomial regression analysis. The model produces logarithmic estimates which have been exponentiated back to counts for the beginning estimates in this table. The additive change for the logarithmic values results in an exponentiated multiplicative factor for estimating change per month.

†The By MMEs model uses ordinary linear regression for the number of MMEs prescribed per month over the 24-month study period.

17

Figure 1: Number of Patients Receiving Opioids by Thresholds & by Type of Prescription

Jul-16

Aug-16

Sep-16Oct-

16

Nov-16

Dec-16

Jan-17

Feb-17

Mar-17

Apr-17

May-17

Jun-17Jul-1

7

Aug-17

Sep-17Oct-

17

Nov-17

Dec-17

Jan-18

Feb-18

Mar-18

Apr-18

May-18

Jun-180

10

20

30

40

50

60

>50MEQs >100MEQs >150MEQs Total LA/ER IR Only

The three line graphs above depict the number of patients receiving a prescriptions by type (LA/ER or IR). Yellow represents the total number of patients who received prescriptions. The next two line graphs are subgroups of the total number of patients. The blue line graph shows the number of patients receiving LA/ER formulations (these patients may have also received IR formulations) and the green line shows the number of patients receiving IR prescriptions only (no LA/ER) over the course of the study.

The area graphs below depict the number of patients who received an opioid quantity above a specified threshold. The blue area graph shows the number of patients receiving >50MMEs of opioids, orange the number of patients receiving >100MMEs and grey the number of patients receiving >150MMEs during the two-year study period. As these area graphs represent patients above certain threshold, patients may fall under multiple categories and thus these graphs are not additive.

18

Figure 2: Total MMEs Dispensed by Month

Jul-16

Aug-16

Sep-16Oct-

16

Nov-16

Dec-16

Jan-17

Feb-17

Mar-17Apr-1

7

May-17

Jun-17Jul-1

7

Aug-17

Sep-17Oct-

17

Nov-17

Dec-17

Jan-18

Feb-18

Mar-18Apr-1

8

May-18

Jun-180

20000

40000

60000

80000

100000

120000

This figure shows the total quantity of opioids dispensed (by MMEs) to all the patients seen in the clinic each month. The solid area graph shows the total number of MMEs dispensed. The patterned area graph tabulates MMEs for LA/ER formulations only and the area graph with diagonal lines includes the total MMEs for IR formulations only. The LA/ER (patterned) and IR graphs (diagonal lines) are additive to form the totals (solid) graph.

Figure 3: Average Daily MMEs Dispensed per Patient per Month

19

Jul-16

Aug-16

Sep-16Oct-

16

Nov-16

Dec-16

Jan-17

Feb-17

Mar-17Apr-1

7

May-17

Jun-17Jul-1

7

Aug-17

Sep-17Oct-

17

Nov-17

Dec-17

Jan-18

Feb-18

Mar-18Apr-1

8

May-18

Jun-180.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

This figure takes the total MMEs prescribed each month divided by the number of patients who received opioids that month and by the number of days in that month to calculate an average daily MME prescribed for each patient. The solid line shows actual averages whereas the dotted line is a line of best fit calculated using linear regression analysis.

20