The

n e w e ng l a n d j o u r na l

of

m e dic i n e

Special Article

Opioid-Prescribing Patterns of Emergency
Physicians and Risk of Long-Term Use
Michael L. Barnett, M.D., Andrew R. Olenski, B.S.,
and Anupam B. Jena, M.D., Ph.D.​​

A BS T R AC T
BACKGROUND

Increasing overuse of opioids in the United States may be driven in part by physician
prescribing. However, the extent to which individual physicians vary in opioid prescribing and the implications of that variation for long-term opioid use and adverse
outcomes in patients are unknown.
METHODS

We performed a retrospective analysis involving Medicare beneficiaries who had
an index emergency department visit in the period from 2008 through 2011 and
had not received prescriptions for opioids within 6 months before that visit. After
identifying the emergency physicians within a hospital who cared for the patients,
we categorized the physicians as being high-intensity or low-intensity opioid prescribers according to relative quartiles of prescribing rates within the same hospital. We
compared rates of long-term opioid use, defined as 6 months of days supplied, in
the 12 months after a visit to the emergency department among patients treated
by high-intensity or low-intensity prescribers, with adjustment for patient characteristics.
RESULTS

Our sample consisted of 215,678 patients who received treatment from low-intensity prescribers and 161,951 patients who received treatment from high-intensity
prescribers. Patient characteristics, including diagnoses in the emergency department, were similar in the two treatment groups. Within individual hospitals, rates
of opioid prescribing varied widely between low-intensity and high-intensity prescribers (7.3% vs. 24.1%). Long-term opioid use was significantly higher among
patients treated by high-intensity prescribers than among patients treated by lowintensity prescribers (adjusted odds ratio, 1.30; 95% confidence interval, 1.23 to 1.37;
P<0.001); these findings were consistent across multiple sensitivity analyses.

From the Department of Health Policy
and Management, Harvard T.H. Chan
School of Public Health (M.L.B.), the Department of Health Care Policy, Harvard
Medical School (A.R.O., A.B.J.), the Division of General Internal Medicine and
Primary Care, Department of Medicine,
Brigham and Women’s Hospital (M.L.B.),
and the Department of Medicine, Massachusetts General Hospital (A.B.J.), Boston,
and the National Bureau of Economic Research, Cambridge (A.B.J.) — all in Massachusetts. Address reprint requests to Dr.
Barnett at the Department of Health Policy and Management, Harvard T.H. Chan
School of Public Health, Kresge Bldg.,
4th Fl., 677 Huntington Ave., Boston, MA
02115, or at ­mbarnett@​­hsph​.­harvard​.­edu.
This article was updated on February 16,
2017, at NEJM.org.
N Engl J Med 2017;376:663-73.
DOI: 10.1056/NEJMsa1610524
Copyright © 2017 Massachusetts Medical Society.

CONCLUSIONS

Wide variation in rates of opioid prescribing existed among physicians practicing
within the same emergency department, and rates of long-term opioid use were increased among patients who had not previously received opioids and received
treatment from high-intensity opioid prescribers. (Funded by the National Institutes of Health.)

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

663

 The

R 

n e w e ng l a n d j o u r na l

ates of opioid prescribing and
opioid-related overdose deaths have quadrupled in the United States over the past
three decades.1-3 This epidemic has increasingly
affected the elderly Medicare population, among
whom rates of hospitalization for opioid overdoses
quintupled from 1993 through 2012.4-6 The risks
of opioid use are particularly pronounced among
the elderly, who are vulnerable to their sedating
side effects, even at therapeutic doses.7 Multiple
studies have shown increased rates of falls, fractures, and death from any cause associated with
opioid use in this population.8-11 Even short-term
opioid use may confer a predisposition to these
side effects and to opioid dependence.12
It is frequently argued that the prescribing behavior of physicians has been a driver of the opioid
epidemic.13,14 Prescribing has increased to the point
that in 2010, enough opioids were prescribed in
the United States to provide every American adult
with 5 mg of hydrocodone every 4 hours for a
month.1,14 This growth may be driven in part by
high variability in physician prescribing of opioids; this variability may reflect overprescribing
beyond what is required for appropriate pain
management.1,15,16 This inconsistency is not surprising, because few clinical guidelines exist, and
there is limited evidence to direct the appropriate use of opioids.17,18 However, few studies have
investigated the extent to which individual physicians vary in opioid prescribing and the implications of that variation for long-term opioid use
and related adverse outcomes in patients.
To examine the extent to which emergency physicians within the same hospital varied in rates of
opioid prescribing, we studied a national sample of
Medicare beneficiaries who received treatment in
an emergency department and who had not used
prescription opioids within 6 months before the
index visit to the emergency department. In order
to understand how initial exposure to an opioid
relates to subsequent outcomes, we identified highintensity and low-intensity opioid prescribers within each hospital and examined rates of long-term
opioid use and future hospitalizations among patients treated by these two groups of prescribers.19,20 To address the challenge of selection bias,
we relied on the fact that patients are unlikely to
choose an emergency department physician once
they have chosen a facility.

664

of

m e dic i n e

Me thods
Study Population

Using the Centers for Medicare and Medicaid Services carrier files for a 20% random sample of
beneficiaries from January 1, 2008, through December 31, 2011, we identified index emergency
department visits at acute care hospitals by Medicare beneficiaries. We defined an index visit as the
earliest visit at which a beneficiary had an evaluation and a management claim by an emergency
medicine physician with a place-of-service designation in the emergency department. Emergency
medicine physicians were defined as physicians
with an emergency medicine specialty who
billed 90% or more of claims with an emergency
department place of service. We included only
one index visit to the emergency department per
beneficiary and excluded all visits to the emergency department that resulted in a hospital admission.
We limited our analyses to beneficiaries who
had been continuously enrolled in Medicare Part D
for 18 months or more, including at least the
period from 6 months before the index visit to
12 months afterward. We included only beneficiaries who had not had an opioid prescription filled
in the 6 months before the index visit. In addition,
we excluded beneficiaries with hospice claims or a
cancer diagnosis between 2008 and 2012.
We assigned each index emergency department
visit to a physician according to the billing National Provider Identifier (NPI) and then linked
each visit to a hospital by matching to facility
claims in the outpatient department file according
to the date and beneficiary. To ensure that we had
an adequate sample size at the physician and hospital level, we excluded physicians with fewer than
five emergency department visits and hospitals
with fewer than five physicians billing for emergency department visits in our sample. If physicians practiced in more than one hospital, they
were assigned to the hospital at which they had the
most visits, and any visits at other facilities were
excluded.
This study was approved by the institutional
review board at Harvard Medical School, which
waived the requirement for informed consent
since the data were deidentified and only aggregate results would be reported.

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

 Opioid Prescribing and Risk of Long-Term Use

Definitions of Opioid Prescriptions
and Intensity of Physicians’ Prescribing

We identified prescription claims corresponding
to an opioid (excluding methadone) according to
the National Drug Code in the Medicare Part D
database.16 We attributed an opioid prescription to
an index emergency department visit and the associated physician if it was filled by the patient
within 7 days after the date of the emergency department visit; in a sensitivity analysis, we restricted this duration to 3 days. This attribution method
was necessary because prescriber NPI information is not available in the Part D database (see
the Methods and Results section in the Supplementary Appendix, available with the full text of
this article at NEJM.org). For this and subsequent
opioid prescriptions, we extracted the number of
days for which opiates were supplied and calculated the morphine equivalents dispensed, using
standard conversion tables.21
We defined the main exposure as treatment
by a “high-intensity” or “low-intensity” opioid prescribing physician within the same hospital. For
each physician, we first calculated the proportion
of all emergency department visits after which an
opioid prescription was filled. We then grouped
physicians into quartiles of rates of opioid prescribing within each hospital and classified physicians as being in the top (high-intensity) or bottom (low-intensity) quartile of prescribing rates. In
93 hospitals, because of a high number of prescribers who did not prescribe opioids, there were
fewer than four separate groups of prescribers to
assign to quartiles; therefore, the highest and lowest prescribers in a hospital were assigned to the
high-intensity and low-intensity groups. We also
defined an alternative exposure, classifying physicians according to the median dose (in morphine
equivalents) of prescriptions filled after an emergency department visit (“high-dose intensity” and
“low-dose intensity”).
Outcomes

Our primary outcome was long-term opioid use,
which we defined as 180 days or more of opioids
supplied in the 12 months after an index emergency department visit, excluding prescriptions
within 30 days after the index visit. We applied this
exclusion because otherwise this outcome, by design, would be correlated with our definition of

the main exposure. We chose 180 days as a specific marker for clinically significant long-term
opioid use beyond the common duration of 90 days
described in previous literature.16,22,23 Therefore,
this outcome captures the extent to which other
physicians prescribe opioids for the subsequent
12 months after a patient’s index emergency department visit.
Secondary outcomes were rates of hospital
encounters (emergency department visits, hospitalizations, or both), including those potentially
related to adverse effects of opioids and those
associated with a selection of medical conditions
that were unlikely to be influenced by opioid use,
in the 12 months after an index emergency department visit (definitions are provided in the Methods
and Results section in the Supplementary Appendix).8-10,24 To assess for possible undertreatment of
pain by low-intensity prescribers that could have
led to repeat emergency care, we also measured
rates of repeat emergency department visits at 14
and 30 days that resulted in the same primary diagnosis as the initial emergency department visit,
classified according to Clinical Classifications Software (CCS) groups (categorizations of codes in the
International Classification of Diseases, 9th Revision).25
Patient Covariates

We collected information on the patients’ age, sex,
race or ethnic group, dual eligibility for Medicaid
and Medicare coverage, and disability status.26 Using the Chronic Conditions Warehouse database,
we also captured the presence of any of 11 chronic
conditions (Table 1) as well as the number of coexisting chronic conditions that a patient had at the
time of an index emergency department visit.
Statistical Analysis

Our strategy to reduce selection bias relied on the
assumption that within the same hospital, patients
do not choose specific emergency physicians, and
therefore patients treated by physicians of varying
opioid prescribing intensity may be similar with
respect to both observable and unobservable characteristics. To assess this approach, we compared
the characteristics of patients who saw high-intensity prescribers with those who saw low-intensity
prescribers. We assessed balance in the case mix
by comparing rates of visits classified according to
the top 25 CCS groups, as well as by plotting the

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

665

 The

n e w e ng l a n d j o u r na l

of

m e dic i n e

Table 1. Characteristics of the Patients, According to Opioid Prescribing Intensity of Physician Seen.*
Patients Treated by
Low-Intensity Prescriber
(N = 215,678)†

Patients Treated by
High-Intensity Prescriber
(N = 161,951)†

P Value‡

68.8±16.3

68.2±16.4

<0.001

Female sex (%)

64.5

64.5

0.75

White race (%)§

76.6

75.9

0.03

Medicare–Medicaid dual eligibility (%)

49.3

49.6

0.19

Disabled (%)

36.4

37.6

<0.001

3.6±2.3

3.5±2.3

<0.001

Acute myocardial infarction

49.8

48.5

<0.001

Alzheimer’s dementia

17.5

16.0

<0.001

Atrial fibrillation

13.6

12.7

<0.001

Cerebrovascular disease

17.3

16.3

<0.001

Chronic kidney disease

20.6

20.1

<0.001

Chronic obstructive pulmonary disease

28.1

27.2

<0.001

Congestive heart failure

30.5

29.0

<0.001

Depression

39.5

39.5

0.94

Diabetes

37.8

37.7

0.69

Characteristic
Age (yr)

Chronic conditions (no.)
Presence of chronic illness (%)

Hyperlipidemia

69.5

69.1

0.09

Hypertension

78.1

77.4

<0.001

Northeast

19.8

20.9

0.03

Midwest

24.4

23.2

0.01

South

38.4

38.6

0.82

West

17.3

17.3

0.97

Census region (%)

*  Plus–minus values are means ±SD.
†  Within each hospital, high-intensity and low-intensity prescribers were defined as the top (high) or bottom (low) quartile of emergency physician prescribers in terms of opioid prescribing. Since many physicians did not prescribe opioids,
some hospitals had many physicians with prescribing rates equal to zero in the low-intensity prescriber group, making
it larger overall than the high-intensity group. Low-intensity prescribers included 8297 physicians; the total number (±SD)
of patients per physician was 26.0±18.6. High-intensity prescribers included 6133 physicians; the total number of patients per physician was 26.4±18.5.
‡  Unadjusted P values were estimated with the use of Student’s t-test for means or the z-test for comparison of proportions.
§  Race or ethnic group was self-reported.

cumulative distribution of the primary-diagnosis
CCS group between high-intensity and low-intensity prescribers. Differences in distributions between groups were assessed with the use of the
Kolmogorov–Smirnov test.27
We then computed unadjusted rates and odds
ratios for each outcome, stratified according to
treatment by a high-intensity or low-intensity
opioid prescriber. To account for residual differences between patient populations, we estimated
adjusted odds ratios with patient-level multivariable logistic regression. Dependent variables were
666

the occurrence of primary or secondary outcomes.
The key explanatory variable was a binary indicator for whether a patient was treated by a highintensity or low-intensity prescriber (the middle
two quartiles of prescribers were not included in
these models). Other covariates included the
patients’ age, sex, race or ethnic group, Medicaid
eligibility, and disability status, as well as the
presence of 11 different chronic conditions. We
prespecified several subgroup analyses to assess
for heterogeneity (Table 2). All models accounted for grouping of patients within hospitals with

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

 Opioid Prescribing and Risk of Long-Term Use

the use of robust standard errors clustered at the
hospital level.28
The hypothetical long-term effect of filling
an initial opioid prescription after an emergency
department visit versus not filling a prescription
was estimated to approximate the number of patients who would need to be prescribed an initial
opioid for one patient to become a long-term user.
A definition of “number needed to harm” is provided in the Methods section in the Supplementary Appendix.29
We performed additional sensitivity analyses
to address the possibility of selection bias and
sensitivity to design assumptions (details are provided in the Methods section in the Supplementary
Appendix). These analyses included an alternative
exposure in which intensity was defined according to the median dose of opioid prescription filled
after an emergency department visit, as described
above. Replicating our results with an alternative
definition of exposure that operates through a
similar causal pathway (increased opioid exposure)
could argue against selection bias, particularly if
the two exposures (dose and frequency of opioid
prescribing) are minimally correlated.
All analyses were performed with the use of
Stata software, version 14.1 (StataCorp). The 95%
confidence intervals around reported estimates
reflect 0.025 in each tail, or P values no higher
than 0.05.

R e sult s
Our sample consisted of 215,678 patients treated
by a low-intensity opioid prescriber and 161,951
patients treated by a high-intensity opioid prescriber during an index emergency department
visit. Overall, the characteristics of patients treated
by high-intensity opioid prescribers were similar
to those of patients treated by low-intensity prescribers, although several differences were significant given the large sample size (Table 1). Diagnoses in patients seen by high-intensity prescribers
and those seen by low-intensity prescribers were
similar (P = 0.87 by the Kolmogorov–Smirnov test
for differences in the distribution of 300 CCS
groups, according to prescriber group [Fig. S1 in
the Supplementary Appendix; for distribution of
the top 25 of 300 CCS diagnosis groups, according
to prescriber group, see Fig. S2 in the Supplementary Appendix]).
On average, rates of opioid prescribing between

low-intensity prescribers and high-intensity prescribers varied by a factor of 3.3 within the same
hospital (7.3% vs. 24.1% of emergency department
visits, P<0.001) (Table 2 and Fig. 1A). Across all
subgroups, prescribing rates among high-intensity prescribers were triple those among low-intensity prescribers. The highest average rate was seen
among patients who visited the emergency department with an injury (23.7%) (Table 2). There
was minimal correlation between physicians’ prescribing rates and the median initial dose of an
opioid prescription that was filled (r = −0.08) (Fig.
S3 and Table S1 in the Supplementary Appendix)
or type of opioid prescribed (Table S2 in the Supplementary Appendix).
Overall, long-term opioid use at 12 months
was significantly higher among patients treated
by high-intensity prescribers than among patients
treated by low-intensity prescribers (1.51% vs.
1.16%; unadjusted odds ratio, 1.31; 95% confidence interval [CI], 1.24 to 1.39) (Table 2 and
Fig. 2). After adjustment, there was minimal
change in this difference (adjusted odds ratio,
1.30; 95% CI, 1.23 to 1.37). This finding corresponds to a number needed to harm of 48 patients receiving an opioid prescription to theoretically lead to 1 excess long-term opioid user.
We observed a stepwise increase in long-term
opioid use with exposure to physicians in each
quartile of opioid prescribing frequency. As compared with the first (low-intensity) quartile, patients treated by physicians in the second quartile had an adjusted odds ratio for long-term opioid
use of 1.10 (95% CI, 1.04 to 1.16) and patients
treated by physicians in the third quartile had an
adjusted odds ratio of 1.19 (95% CI, 1.12 to 1.25)
(Fig. 1B, and Table S3 in the Supplementary Appendix). Differences in long-term opioid use between patients treated by high-intensity prescribers
and those treated by low-intensity opioid prescribers were consistent across subgroups, with minimal change after multivariable adjustment (Fig. 2).
Rates of opioid-related hospital encounters and
encounters for fall or fracture were significantly
higher in the 12 months after an index emergency
department visit among patients treated by highintensity opioid prescribers than among patients
treated by low-intensity opioid prescribers (rates of
any opioid-related encounter, 9.96% vs. 9.73%;
adjusted odds ratio, 1.03; 95% CI, 1.00 to 1.05;
P = 0.02; rates of encounters for fall or fracture,
4.56% vs. 4.28%; adjusted odds ratio, 1.07; 95% CI,

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

667

 The

n e w e ng l a n d j o u r na l

of

m e dic i n e

Table 2. Rate of Filling a Prescription for Opioids within 7 Days after Emergency Department Visit and Rate of Long-Term Use,
According to Opioid Prescribing Intensity of Physician Seen.*
Initial Rate of Opioid Prescription
in Emergency Department

Variable

Average
Rate

Rate of Long-Term Use

Low-Intensity High-Intensity
Prescriber
Prescriber

Low-Intensity
Prescriber

High-Intensity
Prescriber

percent
Overall rate of opioid prescribing
Rate of prescribing according to patient characteristic
Age
<65 yr
65–74 yr
75–84 yr
≥85 yr
Sex
Male
Female
Race
White
Black
Medicare–Medicaid dual eligibility
No
Yes
No. of chronic conditions†
0
1 or 2
≥3
Alzheimer’s disease
No
Yes
Disabled
No
Yes
Depression
No
Yes
Census region
Northeast
Midwest
South
West
Emergency department visit for injury‡
No
Yes

14.7

7.3

24.1

1.16

1.51

17.9
16.4
12.3
8.9

8.8
8.4
6.0
4.3

28.9
26.4
20.4
15.6

2.09
0.86
0.68
0.86

2.82
1.00
0.89
1.01

15.1
14.4

7.6
7.1

24.7
23.8

1.22
1.13

1.53
1.51

14.6
14.9

7.5
6.8

23.7
25.0

1.16
1.38

1.50
1.87

14.4
15.0

7.4
7.2

23.4
24.8

0.71
1.62

0.87
2.17

18.7
16.9
13.3

9.5
8.5
6.5

29.9
27.4
22.0

1.29
1.07
1.17

1.73
1.52
1.48

15.9
8.5

8.0
4.0

25.9
14.7

1.15
1.19

1.52
1.49

13.4
17.0

6.7
8.3

22.1
27.5

0.71
1.95

0.87
2.59

14.8
14.5

7.4
7.1

24.2
23.9

0.78
1.74

1.01
2.28

11.9
14.0
15.9
16.2

5.2
6.9
8.4
7.7

20.5
23.4
25.3
26.7

0.78
1.27
1.33
1.07

1.11
1.45
1.81
1.44

13.7
23.7

6.7
12.6

22.7
35.8

1.16
1.14

1.53
1.42

*  Long-term opioid use was defined as 6 months of days of opioids supplied in the 12 months after an index emergency department visit,
­excluding the first 30 days after the index emergency department visit.
†  The number of chronic conditions among 11 possible conditions is shown. These conditions are the following: acute myocardial infarction,
Alzheimer’s dementia, atrial fibrillation, cerebrovascular disease, chronic kidney disease, chronic obstructive pulmonary disease, congestive
heart failure, depression, diabetes, hyperlipidemia, and hypertension.
‡  An emergency department visit for an injury was defined as any emergency department visit with an “E” code associated with an injury
­(according to the codes in the International Classification of Diseases, 9th Revision). Of patients who visited an emergency department for a
reason other than an injury, 195,651 saw low-intensity prescribers and 144,895 saw high-intensity prescribers. Of patients who visited an
emergency department because of an injury, 20,027 saw low-intensity prescribers and 17,056 saw high-intensity prescribers.

668

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

 Opioid Prescribing and Risk of Long-Term Use

Discussion
In a large, national sample of patients enrolled in
Medicare Part D who received care in an emergency department and who had not used prescription opioids in the 6 months before the visit to
the emergency department, we found substantial
variation in the opioid prescribing patterns of
emergency physicians within the same hospital.
The intensity of a physician’s opioid prescribing
was positively associated with the probability that
a patient would become a long-term opioid user
over the subsequent 12 months. Although our
study was observational, we sought to minimize
selection bias by comparing the characteristics of
patients seen by different emergency physicians
within the same hospital. The association between
physician prescribing rates and increased longterm opioid use was consistent across numerous
subgroups and across all quartiles of physician
prescribing in a dose–response pattern.
It is commonly thought that opioid dependence
often begins through an initial, possibly chance,
exposure to a physician-prescribed opioid, although data from studies to empirically evaluate

this claim are lacking. Our results provide evidence that this mechanism could drive initiation
of long-term opioid use through either increased
rates of opioid prescription or prescription of a
high, versus a low, dose of opioid. Although causality cannot be established from this observaA
Percent of Emergency Department
Visits with an Opioid Prescription

30
25
20
15
10
5
0

1

2

3

4

Prescriber Quartile

B
1.4

Odds Ratio for Long-Term Opioid Use

1.03 to 1.11; P<0.001) (Table 3). There was no
significant difference in 12-month rates of overall hospital encounters or non–opioid-related encounters. Assessment of rates of short-term emergency department revisits for possible evidence
of undertreated pain showed that rates of 14-day
and 30-day repeat emergency department visits
with the same primary diagnosis as the index visit
were similar in the two prescriber groups (P>0.07)
(Table S4 in the Supplementary Appendix).
We replicated the main analysis described above
in multiple other sensitivity analyses, including one
that used a propensity score–matched cohort (see
the Results section in the Supplementary Appendix), although the finding of increased hospital
encounters for falls or fractures with the use of
propensity score matching (P = 0.05) and with the
use of CCS category fixed effects (P = 0.12) was
not significant (Table S6 in the Supplementary
Appendix). In analyses in which physicians were
categorized as being high or low “dose intensity”
prescribers, we found increased odds of long-term
opioid use among patients treated by high doseintensity prescribers (odds ratio, 1.32; 95% CI,
1.19 to 1.46) (Table S6 and Fig. S4 in the Supplementary Appendix).

1.3

1.2

1.1

1.0

1
(Low intensity,
reference)

2

3

Prescriber Quartile

4
(High
intensity)

Figure 1. Prescribing Rates and Adjusted Odds Ratios
for Long-Term Opioid Use, According to Quartile of
Physician Opioid Prescribing.
Panel A shows rates of opioid prescribing by emergency physicians according to within-hospital quartile.
I bars represent 95% confidence intervals. Panel B
shows the adjusted odds ratios and corresponding
95% confidence intervals for rates of long-term opioid
use, according to quartile of physician opioid prescribing. Physicians in each quartile were compared with
those in the lowest prescribing quartile. Odds ratios
were estimated with the use of logistic-regression
models.

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

669

 n e w e ng l a n d j o u r na l

The

of

m e dic i n e

Odds Ratio

Subgroup
Unadjusted

Adjusted (95% CI)

1.31

1.30 (1.23–1.37)

1.36
1.16
1.32
1.18

1.37 (1.27–1.48)
1.17 (1.03–1.32)
1.33 (1.15–1.54)
1.18 (0.98–1.42)

1.26
1.34

1.25 (1.14–1.37)
1.33 (1.24–1.43)

1.29
1.36

1.28 (1.20–1.37)
1.35 (1.19–1.53)

1.43
1.14
1.37
1.35

1.42 (1.23–1.64)
1.12 (1.00–1.27)
1.36 (1.25–1.48)
1.34 (1.17–1.53)

1.35
1.43
1.26

1.36 (1.15–1.61)
1.41 (1.26–1.58)
1.25 (1.17–1.34)

1.22
1.35

1.20 (1.08–1.33)
1.34 (1.26–1.44)

1.30
1.32

1.29 (1.18–1.41)
1.30 (1.21–1.40)

1.32
1.25

1.30 (1.23–1.38)
1.25 (1.05–1.49)

1.32
1.26

1.31 (1.23–1.39)
1.26 (1.09–1.44)

1.23
1.33

1.24 (1.13–1.36)
1.34 (1.25–1.44)

Overall opioid use at 12 mo
Age
<65 yr
65–74 yr
75–84 yr
≥85 yr
Sex
Male
Female
Race
White
Black
Census region
Northeast
Midwest
South
West
No. of chronic conditions
None
1 or 2
≥3
Medicare–Medicaid dual eligibility
No
Yes
Depression
No
Yes
Emergency department visit for injury
No
Yes
Alzheimer’s disease
No
Yes
Disabled
No
Yes
0.8

1.2

1.6

2.0

Odds Ratio (95% CI)

Figure 2. Unadjusted and Adjusted Odds Ratios for Long-Term Opioid Use, According to Treatment by High-Intensity
or Low-Intensity Opioid Prescriber.
All unadjusted odds ratios were estimated with the use of bivariate logistic regression with the occurrence of longterm opioid use as the dependent variable and exposure to a high-intensity provider as the key explanatory variable.
All adjusted models had further adjustment for the patients’ age, sex, race or ethnic group, Medicare–Medicaid
dual eligibility, and disability status and the presence of 11 chronic conditions.

tional study, if our results represent a causal relationship, for every 48 patients prescribed a new
opioid in the emergency department who might
not otherwise use opioids, 1 will become a longterm user; this is a low number needed to harm
for such a common therapy.
Of course, prescriptions provided by other physicians in the months after an emergency department visit are necessary for long-term opioid use
670

to take hold. Conversion to long-term use may be
driven partly by clinical “inertia” leading outpatient clinicians to continue providing previous
prescriptions. Such clinical inertia may affect only
a narrow segment of the population; this could
explain why rates of initial opioid prescribing may
vary by a factor of three, whereas long-term use
varies by only approximately 30%. It is important to acknowledge that patients treated by high-

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

 Opioid Prescribing and Risk of Long-Term Use

Table 3. Hospital Encounters within 12 Months after an Index Emergency Department Visit to a Low-Intensity or HighIntensity Opioid Prescriber.*
Type of Hospital Encounter

12-Mo Encounter Rate

Adjusted Odds Ratio
(95% CI)†

P Value†

Patients Treated Patients Treated
by Low-Intensity by High-Intensity
Prescriber
Prescriber
(N = 215,678)
(N = 161,951)
% of patients
Any hospital encounter

60.5

60.3

0.99 (0.97–1.00)

0.13

Any hospitalization

46.1

45.8

0.99 (0.97–1.00)

0.15

Any emergency department visit

57.4

57.1

0.99 (0.97–1.00)

0.07

Any opioid-related hospital encounter

9.73

9.96

1.03 (1.00–1.05)

0.02

Fall or fracture

4.28

4.56

1.07 (1.03–1.11)

<0.001

Constipation

4.16

4.11

0.99 (0.96–1.02)

0.44

Respiratory failure

2.04

2.01

0.98 (0.94–1.03)

0.46

Opioid poisoning

0.07

0.10

1.40 (1.12–1.74)

<0.001

11.77

11.75

1.00 (0.98–1.02)

0.85

Hyperglycemia

0.24

0.24

0.99 (0.87–1.14)

0.93

Urinary tract infection

1.08

1.13

1.04 (0.98–1.11)

0.17

Atrial fibrillation

6.48

6.39

0.98 (0.95–1.01)

0.24

Stroke

4.12

4.08

0.99 (0.96–1.02)

0.52

Any selected non–opioid-related
hospital encounter

*  A hospital encounter refers to a hospitalization or an emergency department visit. Definitions are provided in the
Methods section in the Supplementary Appendix. CI denotes confidence interval.
†  Adjusted odds ratios and P values were estimated with the use of logistic-regression models with occurrence of an opioid-related hospitalization in the 12 months after an emergency department visit as the dependent variable. The key covariate was an indicator for being seen by a high-intensity or a low-intensity prescriber. All models were adjusted for
age, sex, race and ethnic group, Medicare–Medicaid dual eligibility, and the presence of 11 chronic conditions.

intensity opioid prescribers may have disproportionately required appropriate opioid therapy,
although some of the variation we observed in
rates of opioid prescribing may also indicate overuse. If differences in appropriate use were a major
driver of variation in prescribing, we may have
expected increased rates of short-term emergency
department revisits due to inadequately treated
pain among the low-intensity prescriber groups.
However, we did not find a difference in such rates
between prescriber groups; this suggests that increased opioid prescribing did not prevent revisits to the emergency department.
Our results are unlikely to be explained by selection bias for several reasons. First, our analysis
focused on variation in opioid prescribing of emergency physicians within the same hospital; these
physicians are unlikely to select or attract systematically different patient populations. Second, even

though there were small differences in characteristics between patients treated by the two prescriber groups, adjustment for these characteristics did not change our results with respect to
long-term opioid use. We also replicated our results in a range of sensitivity analyses, and the case
mix of emergency department visits across 300
diagnosis categories was statistically indistinguishable between groups. In addition, we replicated our
results with an alternative exposure definition
based on opioid dose; this exposure was minimally correlated with the frequency of opioid prescribing by physicians.
Our study has several limitations. Most important, this is an observational study and cannot be
interpreted as causal, although our findings were
robust in several sensitivity analyses addressing
selection bias. Second, since we could not observe whether an opioid prescription was appro-

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

671

 The

n e w e ng l a n d j o u r na l

priate, our ability to quantify the extent of overuse of opioids was limited. Third, because we
focused on Medicare patients with Part D enrollment and emergency department visits, our results may not be generalizable to other populations. However, the growing prevalence of opioid
misuse among the elderly makes this an important group to study.16,24 Fourth, the association
between high-intensity opioid prescribers and opioid-related hospital encounters within 12 months
after the index emergency department visit was
small and not significant in some sensitivity analyses. In addition, for outcomes with a significant
association, the absolute difference in rates of
hospital encounters between groups was small
(e.g., an absolute difference of 0.23 percentage
points for any opioid-related encounter) (Table 3).
Therefore, we have weaker evidence to support
this association than our results on long-term opioid use, and if it were causal, the clinical magnitude of this association would be small. Fifth,
we were unable to unequivocally attribute an
opioid prescription to an emergency physician;
however, our analyses were robust with respect
to a stricter threshold of 3 days to fill a prescription for opioids from an emergency physician.16
References
1.  Vital signs:​overdoses of prescription
opioid pain relievers — United States,
1999–2008. MMWR Morb Mortal Wkly
Rep 2011;​60:​1487-2.
2.  Prescription opioid overdose data. Atlanta:​Centers for Disease Control and
Prevention, 2016 (http://www​.cdc​.gov/​
drugoverdose/​data/​overdose​.html).
3.  National Institute on Drug Abuse.
America’s addiction to opioids:​heroin and
prescription drug abuse. May 14, 2014
(https:/​/​w ww​.drugabuse​.gov/​about-nida/​
legislative-activities/​testimony-to
-congress/​2016/​americas-addiction-to
-opioids-heroin-prescription-drug-abuse).
4.  Healthcare Cost and Utilization Project. Hospital inpatient utilization related
to opioid overuse among adults, 19932012. August 2014 (https:/​/​w ww​.hcup-us​
.ahrq​.gov/​reports/​statbriefs/​sb177
-Hospitalizations-for-Opioid-Overuse​.jsp).
5.  Vestal C. Older addicts squeezed by
opioid epidemic. Stateline. July 26, 2016
(http://pew​.org/​2asv3TO).
6.  Dufour R, Joshi AV, Pasquale MK, et
al. The prevalence of diagnosed opioid
abuse in commercial and Medicare managed care populations. Pain Pract 2014;​
14(3):​E106-E115.
7.  Makris UE, Abrams RC, Gurland B,

672

of

m e dic i n e

Finally, our statistical tests do not account for the
false positive rate associated with multiple secondary analyses; therefore, P values should be regarded as exploratory.
In conclusion, we found variation by a factor
of more than three in rates of opioid prescribing
by emergency physicians within the same hospital
and increased rates of long-term opioid use among
patients treated by high-intensity opioid prescribers. These results suggest that an increased likelihood of receiving an opioid for even one encounter
could drive clinically significant future long-term
opioid use and potentially increased adverse outcomes among the elderly. Future research may
explore whether this variation reflects overprescription by some prescribers and whether it is
amenable to intervention.
Supported by a grant from the Office of the Director, National Institutes of Health (NIH) (NIH Director’s Early Independence Award, 1DP5OD017897-01, to Dr. Jena).
Dr. Barnett reports serving as medical advisor to and holding
stock in Ginger.io; and Dr. Jena, receiving consulting fees from
Pfizer, Hill-Rom Services, Bristol-Myers Squibb, Novartis Pharmaceuticals, Vertex Pharmaceuticals, and Precision Health Economics. No other potential conflict of interest relevant to this
article was reported.
Disclosure forms provided by the authors are available with
the full text of this article at NEJM.org.

Reid MC. Management of persistent pain
in the older patient: a clinical review.
JAMA 2014;​312:​825-36.
8.  Miller M, Stürmer T, Azrael D, Levin
R, Solomon DH. Opioid analgesics and
the risk of fractures in older adults with
arthritis. J Am Geriatr Soc 2011;​59:​430-8.
9.  Solomon DH, Rassen JA, Glynn RJ,
Lee J, Levin R, Schneeweiss S. The comparative safety of analgesics in older
adults with arthritis. Arch Intern Med
2010;​170:​1968-76.
10. Buckeridge D, Huang A, Hanley J, et
al. Risk of injury associated with opioid
use in older adults. J Am Geriatr Soc 2010;​
58:​1664-70.
11.  O’Neil CK, Hanlon JT, Marcum ZA. Adverse effects of analgesics commonly used
by older adults with osteoarthritis: focus on
non-opioid and opioid analgesics. Am J
Geriatr Pharmacother 2012;​10:​331-42.
12. Butler MM, Ancona RM, Beauchamp
GA, et al. Emergency department prescription opioids as an initial exposure
preceding addiction. Ann Emerg Med
2016;​68:​202-8.
13. Frank R. Targeting the opioid drug
crisis:​a Health and Human Services initiative. Health Affairs Blog. April 3, 2015
(http://healthaffairs​.org/​blog/​2015/​04/​03/​

targeting-the-opioid-drug-crisis-a-health
-and-human-services-initiative/​).
14. Department of Health and Human
Services Behavioral Health Coordinating
Committee. Addressing prescription drug
abuse in the United States: current activities and future opportunities. 2014 (https:/​/​
www​.cdc​.gov/​drugoverdose/​pdf/​hhs
_prescription_drug_abuse_report_09​
.2013​.pdf).
15. Kuo Y-F, Raji MA, Chen N-W, Hasan
H, Goodwin JS. Trends in opioid prescriptions among Part D Medicare recipients
from 2007 to 2012. Am J Med 2016;​129(2):​
221.e21-221.e30.
16. Jena AB, Goldman D, Karaca-Mandic
P. Hospital prescribing of opioids to
Medicare beneficiaries. JAMA Intern Med
2016;​176:​990-7.
17. Poon SJ, Greenwood-Ericksen MB.
The opioid prescription epidemic and the
role of emergency medicine. Ann Emerg
Med 2014;​64:​490-5.
18. Tamayo-Sarver JH, Dawson NV, Cydulka RK, Wigton RS, Baker DW. Variability in emergency physician decision
making about prescribing opioid analgesics. Ann Emerg Med 2004;​43:​483-93.
19. Pletcher MJ, Kertesz SG, Kohn MA,
Gonzales R. Trends in opioid prescribing

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

 Opioid Prescribing and Risk of Long-Term Use

by race/ethnicity for patients seeking care
in US emergency departments. JAMA
2008;​299:​70-8.
20.  Hoppe JA, Nelson LS, Perrone J, Weiner SG. Opioid prescribing in a cross section of US emergency departments. Ann
Emerg Med 2015;​66(3):​253-259.e1.
21. Dowell D, Haegerich TM, Chou R.
CDC guideline for prescribing opioids for
chronic pain — United States, 2016.
JAMA 2016;​315:​1624-45.
22. Dunn KM, Saunders KW, Rutter CM,
et al. Opioid prescriptions for chronic
pain and overdose: a cohort study. Ann
Intern Med 2010;​152:​85-92.
23. Braden JB, Russo J, Fan M-Y, et al.

Emergency department visits among recipients of chronic opioid therapy. Arch
Intern Med 2010;​170:​1425-32.
24.  Jena AB, Goldman D, Weaver L, Karaca-Mandic P. Opioid prescribing by multiple providers in Medicare: retrospective
observational study of insurance claims.
BMJ 2014;​348:​g1393.
25.  Healthcare Cost and Utilization Project. Clinical Classifications Software (CCS)
for ICD-9-CM (https:/​/​w ww​.hcup-us​.ahrq​
.gov/​toolssoftware/​ccs/​ccs​.jsp).
26.  Meara E, Horwitz JR, Powell W, et al.
State legal restrictions and prescriptionopioid use among disabled adults. N Engl
J Med 2016;​375:​44-53.

27. National Institute of Standards and
Technology. Kolmogorov-Smirnov Goodness-of-Fit Test. 2013 (http://www​.itl​.nist​
.gov/​div898/​handbook/​eda/​section3/​
eda35g​.htm).
28.  White H. A heteroskedasticity-consistent covariance matrix and a direct test for
heteroskedasticity. Econometrica 1980;​48:​
817-38.
29.  Angrist JD, Pischke J-S. Mostly harmless econometrics: an empiricist’s companion. Princeton, NJ:​Princeton University Press, 2008.
Copyright © 2017 Massachusetts Medical Society.

Tiger, Kanha National Park, India 

Anil Sain, M.D.

n engl j med 376;7 nejm.org  February 16, 2017

The New England Journal of Medicine
Downloaded from nejm.org on August 15, 2017. For personal use only. No other uses without permission.
Copyright © 2017 Massachusetts Medical Society. All rights reserved.

673