Pain Medicine 2015; 16: 1720–1731
Wiley Periodicals, Inc.
PRIMARY CARE & HEALTH SERVICES SECTION
Simon Holliday, BMed, FAChAM, FRACGP,
FACRRM,*,† Simon Morgan, MPH&TM, FRACGP,‡
Amanda Tapley, Biomedical Science(Hons),‡
Adrian Dunlop, PhD, FAChAM,*,†
Kim Henderson, BNurs, Grad Dip. Health Soc.
Sci.,‡ Mieke van Driel, PhD, FRACGP,§
Neil Spike, MBBS, FRACGP,¶,**
Lawrie McArthur, MBBS, FRACGP,††
Jean Ball, BMath, Grad Dip Med Stat,‡‡
Chris Oldmeadow, PhD, BMathematics
(Honours),§§ and Parker Magin, PhD, FRACGP*,‡
*School of Medicine and Public Health, Faculty of
Health, University of Newcastle, Newcastle, NSW,
Australia; †Drug and Alcohol Clinical Services, Hunter
New England Local Health District, NSW, Australia;
‡
General Practice Training Valley to Coast, Mayfield,
NSW, Australia; §Discipline of General Practice,
School of Medicine, The University of Queensland,
Brisbane, Queensland, Australia; ¶Department of
General Practice, The University of Melbourne,
Victoria, Australia; **Director of Medical Education and
Training, VMA General Practice Training, Melbourne,
Victoria, Australia; ††Director of Medical Education
and Training, Adelaide to Outback GP Training,
Adelaide, South Australia; ‡‡Clinical Research Design,
Information Technology and Statistical Support
(CReDITSS), Hunter Medical Research Institute,
Newcastle, NSW, Australia; §§Centre for Clinical
Epidemiology and Biostatistics, University of
Newcastle, Newcastle, NSW, Australia
Reprint requests to: Dr. Simon Mark Holliday, Albert
St Medical Centre, 78 Albert St Taree NSW 2430
Taree, New South Wales 2430, Australia, Tel: 161
(0)2 6552 5533; Fax: 161 (0)2 6552 4249; Email:
simon.holliday@albertstmc.com,
Simon.Holliday@hnehealth.nsw.gov.au.
This article was published online on 27 June 2015. An
error was subsequently identified. This notice is
included in the online and print versions to indicate
that both have been corrected on 11 July 2015.
1720
Declaration of Conflicting Interests: This study was
supported by a competitive grant from the Mental
Health, Drugs and Alcohol Office of the NSW Ministry
of Health. The project is also funded by the
participating educational organisations: General
Practice Training Valley to Coast, the Victorian
Metropolitan Alliance, General Practice Training
Tasmania, and Adelaide to Outback GP Training
Program. These organisations are funded by General
Practice Education and Training, an Australian
Commonwealth Government initiative.
Abbreviations: OST 5 Opioid Substitution Therapy;
PNCP 5 Persistent Non-Cancer Pain; QUM 5 Quality
Use of Medicines; PBS 5 Pharmaceutical Benefits
Scheme; ReCEnT 5 Registrar (GP Trainee) Clinical
Encounters in Training; MIMWOs 5 Medical Issue
managed with opioids; ACSQHC 5 The Australian
Commission on Safety and Quality in Health Care;
RTPs 5 Regional Training Providers
Abstract
Objective. With escalating opioid prescribing come
individual and public health harms. To inform quality improvement measures, understanding of opioid
prescribing is essential. We aimed to establish
consultation-level prevalence and associations of
opioid prescribing.
Design. A cross-sectional secondary analysis from
a longitudinal multisite cohort study of general
practitioner (GP) vocational trainees: “Registrar
Clinical Encounters in Training.”
Setting. Four of Australia’s seventeen GP Regional
Training Providers, during 2010-13.
Subjects. GP trainees.
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Original Research Article
The Pattern of Opioid Management by
Australian General Practice Trainees
Pattern of Opioid Management
Methods. Practice and trainee demographic data
were collected as well as patient, clinical and educational data of 60 consecutive consultations of each
trainee, each training term. Outcome factors were
any opioid analgesic prescription and initial opioid
analgesic prescription for a specific problem for the
first time.
Conclusions. Most opioids were prescribed as
maintenance therapy for non-cancer pain. Demographic associations with opioid analgesic prescribing resemble those presenting for opioid
dependency treatment. Our findings should
inform measures by regulators and medical
educators
supporting
multimodal
pain
management.
Key Words. Opioids; Analgesic; Pain Management;
Primary Care; Quality of Health Care; Prescriptions; Persistent Pain; Risk Factors; Aboriginal
and Torres Strait Islander
Background
Pleasure and pain have been improved or treated by
opium for millennia. Still, opioid therapeutic use remains a
conundrum [1–3]. Opioid management has evolved into
several therapeutic models. Opioid Substitution Therapy
(OST) is a highly structured and evidence based model of
care to minimize harms from opioid dependency [4]. OST
remains relatively inaccessible with few (2.1%) Australian
doctors authorized to prescribe it [5,6]. Barriers to GPs
becoming OST prescribers frequently include stigma,
workload, lack of specialist support or perceived threat
[7]. The specialty of palliative care developed a model of
opioid analgesia based on self-titration and minimal monitoring [8]. The specialty of pain medicine adopted this
model to address the under-treatment of chronic pain,
regardless of etiology [2,9,10]. The Australian National
Pain Strategy claims that less than 10% of Persistent
Concurrent with claims of under-treatment, opioid prescribing in Australia increased 15-fold between 1992
and 2012 [15]. During this time pharmaceutical opioids
became the most common cause (69%) of accidental
opioid overdoses, especially in older age groups [16].
An Australian study tested assertions that most prescribing was for PNCP [17] and found that only a minority of opioid prescribing was for “chronic” conditions
43.9%) or malignant neoplasms (3.5%) [18]. Whether
opioid maintenance is indicated in PNCP is increasingly
contested [12,19,20] with one recent guideline stating
PNCP is currently not an evidence-based indication for
long term analgesics [21]. Similarly, a systematic review
by the United States Food and Drug Administration
found no adequate or well-controlled studies of opioid
use longer than 12 weeks [22].
A number of Australian studies have showed that opioid
analgesics are frequently prescribed outside Pharmaceutical Benefits Scheme (PBS) subsidization criteria or
prescribing guidelines [13,15,21,23,24]. Regulators have
responded in various ways. Of disciplinary cases where
doctors have been sanctioned by tribunals, 25% involve
inappropriate prescribing, mainly drugs of dependency
[25,26]. In the USA, the Centers for Disease Control
and Prevention is currently establishing prescribing
norms to identify and intervene in cases of patients or
providers who fall outside these norms [27]. The Australian Commission on Safety and Quality in Health Care
(ACSQHC) is similarly investigating mapping variations in
opioid prescribing (Dr. Heather Buchan: director: personal communication 11/8/2014). Mapping such variations
in opioid prescribing is predicated on detailed and reliable prescribing data. The ACSQHC has established the
Quality Use of Medicines (QUM) framework to consider
how pharmaceutical options are chosen by clinicians,
which medicines are selected and whether the use of
these medicines is both safe and effective [28].
To improve access to selected medications for specific
indications, the Commonwealth Government provides
Australian residents subsidies through the PBS. Data
from the PBS is readily accessible to researchers but
1721
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Results. Overall, 645 trainees participated.
Opioids comprised 4.3% prescriptions provided
for 3.8% of patients. Most frequently prescribed
were codeine (39.9%) and oxycodone (33.4%).
Prescribing was for acute pain (29.3%), palliative
care (2.6%) or other indications (68.1%). Most prescribing involved repeat prescriptions for preexisting problems (62.7% of total). Other associations included older patients; prescriber and
patient male gender; Aboriginal/Torres Strait
Islander status; rural and disadvantaged locations; longer consultations; and generation of
referrals, follow-up, and imaging requests. Opioid
initiation was more likely for new patients with
new problems, but otherwise associations were
similar. Trainees rarely reported addiction riskmitigation strategies.
Non-Cancer Pain (PNCP) patients, gain access to effective analgesia [11]. The strategy champions access to
opioids for “legitimate users” [11]. Notwithstanding the
evidence that opioid-related mortality is associated with
different variables [10], the strategy proscribes their long
term provision in the context of “predominantly psychological
factors,”
“inappropriate”
prescribing
or
“unsanctioned use” [11]. Such a judgement, reliant on a
clinician’s experience, prejudices and knowledge [1], may
be especially problematic for “uninitiated prescribers due
to the lack of a useful case definition for the various
dependent states” [12]. Australian GPs report infrequently
effecting such judgements [13], with associated barriers
involving the beliefs and stigmatizing attitudes of clinicians
and time pressures, similar as towards OST [7,14]. Financial and geographical barriers to accessing multidisciplinary pain management are also involved [11].
Holliday et al.
significantly under-estimates prescribing. This is
because it does not capture unsubsidized medications,
any medications where the dispensed price is below the
patient contribution (including many older and cheaper
opioids) or “private” or inpatient prescriptions written
outside the public community dispensing framework
[29]. PBS-data also does not facilitate linkage to individual general practitioner (GP) or patient data [17,29].
Methods
This analysis was conducted within the Registrar Clinical
Encounters in Training (ReCEnT) project. ReCEnT is an
ongoing multisite cohort study of GP trainees’ (registrars’) in-consultation clinical experience.
The Australian General Practice Training Program involves
a minimum of three years training, of which at least three
six-month Full Time Equivalent terms must be spent in general practice rather than hospital-based practice. The general practice component is delivered by 17 geographically
defined Regional Training Providers (RTPs) and their affiliated training general practices and leads to Fellowship of
either the Royal Australian College of General Practitioners
and/or the Australian College of Rural and Remote Medicine. Though GP trainees have recourse to their supervisors for advice and support in an “apprenticeship-like
model,” they function as independent practitioners (including for prescribing). The trainees in the ReCEnT study are
from four of Australia’s seventeen RTPs spanning four of
Australia’s eight states and territories (New South Wales,
Victoria, South Australia and Tasmania).
One state, Tasmania, commenced rolling-out a real-time
prescription monitoring system in 2010, [32] and now
approximately one third of Tasmanian general practices
have requested it and have access to it (Clinical Associate Professor Adrian Reynolds: Tasmanian clinical director of alcohol and drug services: personal
communication 8/4/2015). The study encompasses
general practices across all rural-urban classifications
from Major City to Very Remote [33].
The ReCEnT methodology has been described in detail
elsewhere [34]. Briefly, GP trainees record the details of
60 consecutive clinical consultations with a paper1722
Data in the current analysis is from eight rounds of data
collection, 2010-13 and was confined to patients 16
years of age or older reflecting the sparsity of research,
specialist treatment centers and GP opioid analgesia
prescribing for the younger demographic [11,18].
Outcome Factors
The primary outcome factor in this study was the prescription of an opioid for pain management. We
selected these opioids using the “N02A” and “N01AH”
codes from the N (nervous) section of International Anatomical Therapeutic Chemical [35]. We excluded opioids
(codeine) used as cough suppressants (code
“R05DA04”) or used for addictive disorders (codes
“NO7B A-C”) a priori, as our primary focus was analgesia [35]. The secondary outcome factor was the “initial”
prescription of an opioid for analgesia. A prescription
was classified as “initial” when used for the first time for
that specific medical issue (accepting that the medicine
may have been used for that patient for a different medical issue). If a medicine was a continuation or repeat of
previous therapy, (signifying historical as well as current
prescribing decisions), it was classified as “continuing.”
Independent Variables
Independent variables collected related to trainee,
patient, practice, and consultation.
Trainee factors were age, gender, training term, country of
medical qualification (Australia/international), and the number of half-days worked per week. Practice factors included
practice size (number of full-time equivalent GPs), and if the
practice routinely bulk-billed (i.e., the Commonwealth government completely reimburses a practitioner for the consultation leaving no financial cost to the patient). Postcode
was used to define practice rurality/urbanicity using the
Australian
Standard
Geographical
ClassificationRemoteness Area classification of the practice location [33]
and the practice location’s Socioeconomic Index for Area
(SEIFA) Relative Index of Disadvantage [36]. Patient factors
were age, gender, Aboriginal/Torres Strait Islander status,
non-English speaking background and whether new to the
practice or the trainee. Consultation factors were duration
of consultation in minutes as estimated and recorded by
the trainee, the number of medical issues managed, the
ordering of imaging, the number of pathology tests ordered
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Due to the limitations of PBS-level data, calls have been
made for population based studies to explore individual
level data and identify risky patterns of opioid prescribing particularly to disadvantaged populations
[15,26,29,30]. Such studies would inform interventions
to promote future QUM but frequently suffer from low
GP response rates [31]. Such interventions must involve
early-career GPs as they comprise largely “uninitiated”
prescribers whose opioid prescribing has been identified
as challenging [12] and less guideline concordant [13].
In this study of Australian GP trainees, we aim to map
the prevalence, nature and associations of opioid analgesic prescribing by using contemporaneously recorded
detailed individual GP trainee consultation data.
based encounter form at approximately the mid-point of
each six-month general practice training term. As data
collection is designed to reflect a “normal” week of general practice, consultations in specialized clinics (e.g.,
vaccinations) are excluded. Only office-based consultations, not home or nursing home visits, are included. Initial data collection involves demographic, education,
work experience and current practice characteristics.
The encounter form data encompasses four broad
areas: patient demographics, diagnoses or problems
managed (hereafter referred to as medical issues), management, and educational training aspects.
Pattern of Opioid Management
Table 1
Participating trainee, trainee-term, and practice characteristics
Variable
Trainee variables (n 5 645)
Trainee gender
Pathway trainee enrolled in
Trainee age (years)
Trainee year of graduation
Trainee-term or practice-term variables (n 5 1426)
Trainee training term
Trainee worked at the practice previously
Trainee works fulltime
Does the practice routinely bulk bill
Number of GPs working at the practice
Rurality of practice
SEIFA*
n% (95% CIs) or Mean (SD)
Male
Female
General
Rural
No
Yes
Mean (SD)
Mean (SD)
202 34.1% (30.4–37.8)
425 65.9% (62.2–69.6)
494 77.0% (73.7–80.2)
148 23.0% (19.8–26.3)
155 24.4% (21.1–27.8)
480 75.6% (72.2–78.9)
32.8 (6.6)
2005.1 (5.6)
Term 1
Term 2
Term 3
Term 4
No
Yes
No
Yes
No
Yes
1–4
5–101
Major city
Inner regional
Outer regional or remote
Mean (SD)
557 39.1% (36.5–41.6)
488 34.2% (31.8–36.7)
306 21.5% (19.3–23.6)
75 5.3% (4.1–6.4)
994 70.7% (68.3–73.0)
413 29.3% (27.0–31.7)
302 21.7% (19.5–23.8)
1091 78.3% (76.1–80.5)
1179 83.4% (81.5–85.4)
234 16.6% (14.6–18.5)
454 32.5% (30.1–35.0)
941 67.5% (65.0–69.9)
827 58.0% (55.4–60.6)
424 29.7% (27.4–32.1)
175 12.3% (10.6–14.0)
5.4 (2.8)
* Socioeconomic Index for Area (SEIFA) relative index of disadvantage: lower deciles are relatively disadvantaged.
or whether a referral or scheduled follow-up was made.
Educational factors included whether the trainee sought
assistance during the consultation or generated learning
goals for subsequent attention.
Registrars could code each problem as either “new” or
“pre-existing.” The former included initial episodes,
exacerbations of recurrent problems, or any problems
(regardless of duration) which were being managed for
the first time. The medical issues nominated by the
registrars were coded according to the International
Classification of Primary Care (second edition) (ICPC-2
PLUS) system [37]. This is the international standard for
classifying primary care data and the validity of this system has previously been demonstrated [38]. Individual
diagnoses/problems are grouped in 17 Disease Chapters and further classified as process codes, symptoms/
complaints, infections, neoplasms, injuries, congenital
anomalies, and other diagnoses. Chronic diseases in
our study were coded via a classification system derived
from ICPC-2 [39]. We also manually reviewed all the
coded diagnoses to identify those which related to palliative care or acute pain (taken to include: acute trauma;
any injury classified by the trainee as “new”; or issues
identified as preoperative or postoperative) with the
remainder regarded as PNCP.
Statistical Analysis
This was a cross-sectional analysis of patient consultations from the longitudinal ReCEnT study. The unit of
analysis for both primary and secondary outcomes was
the individual medical issue rather than the individual
consultation.
Percentages of trainees’ medical issues managed with
opioids (MIMWOs) were calculated, with 95% confidence intervals. To test associations of an opioid being
prescribed, simple and multiple logistic regression were
used within a generalized estimating equations (GEE)
framework. GEE is a parameter estimation technique
that accounts for the lack of independence in the data
due to clustering from repeated measures from registrars. In GEEs a working correlation matrix is specified
(i.e., a model for the within-cluster correlation) and this is
used to re-estimate the regression parameters and
standard errors. We have used the compound symmetry
working correlation matrix, in this analysis; this assumes
the same correlation parameter for all repeated measures, but these methods are robust to misspecification.
All independent variables (above) with a p value less
than 0.20 and a relevant effect size in the univariate
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Qualified as a doctor in Australia
Class
Holliday et al.
Table 2
Opioids prescribed
Frequency
codeine combinations
oxycodone
tramadol
buprenorphine
morphine
fentanyl
other*
Total
1,145
1002
337
203
117
95
33
2,932
Actual % of
opioids (95% CI)
39.05 (36.82–41.38)
34.17 (32.09–36.36)
11.49 (10.30–12.79)
6.92 (6.00–7.94)
3.99 (3.30–4.78)
3.24 (2.62–3.96)
1.13 (0.77–1.58)
* Other opioids include: dihydrocodeine, hydromorphone,
pethidine.
analysis were included in the multiple regression model.
Variables which had a small effect size and were no longer significant in the multivariate model were removed
from the final model as long as the variable’s removal
did not change the resultant model. The SEIFA variable
was used as a continuous variable with 10 deciles.
Consultation duration was also analyzed as a continuous variable. Linearity assumptions for the continuously
modelled variables were assessed using graphical
inspections of predicted probabilities over the range of
the variables values. Odds ratios (ORs) were presented
with 95% confidence intervals (CIs).
Two regression models were built (using all available
data), the first with dependent variable “any opioid prescribed.” This analysis included all medical issues. The
dependent variable in the second model was “initial
opioid prescribed.” This analysis excluded MIMWOs
where the prescription was “continuing.” Statistical analyses used SAS v9.3. Correlates were considered statistically significant if the P-value was < 0.05.
Ethics Approval
The ReCEnT project has approval from the University of
Newcastle Human Research Ethics Committee, Reference H-2009-0323.
Results
In all, there were 1,426 training term recording cycles
(including details of 69,621 individual consultations,
112,890 medical issues and 68,582 medications prescribed) contributed to by 645 individual trainees
(response rate 94.0%). The demographics of the participating trainees and practices are presented in Table 1.
Overall Findings
Opioids were prescribed in 2660 (3.8% of total) consultations and 2,675 (2.4% of total) medical issues,
1724
Opioids were prescribed for 372 individual ICPC-2
PLUS conditions. Our manual review of these found
opioid analgesics were provided for acute, palliative and
PNCP indications in 29.3%, 2.6%, and 67.7% of total
MIMWOs, respectively. For PNCP, the most common
MIMWOs was back complaints (609/22.8% of MIMWOs). Some (15/0.6% MIMWOs) opioid analgesics
were provided for medical issues classified in terms of
addiction rather than pain. Other diagnoses are presented in Table 3.
General Prescribing Associations
The univariate associations of a MIMWO are presented
in Table 4 with the multivariate model presented in
Table 5. In the adjusted model, variables associated
with MIMWOs at the 5% significance threshold were:
Patient factors: Older age (OR: 1.9 CI: 1.7–2.2 & 1.8 CI:
1.6–2.1, respectively for 35–64 & 651 compared to 16–
34), male gender (OR: 1.2 CI: 1.1–1.3), and Aboriginal
and Torres Strait Islander status (OR: 2.2 CI: 1.6–2.9).
Trainee factor: Male gender (OR: 1.2 CI: 1.6–2.9).
Practice factors: Outer regional, remote or very remote
location (OR: 1.3 CI: 1.1–1.7) (as compared to major
cities) and a lower SEIFA score (i.e., relative disadvantage) (OR: 0.97 for each decile increase CI: 0.950–
0.998).
Consultation factors: new medical problems (OR: 0.4
CI: 0.36–0.44), “chronic” problems (OR: 0.6 CI:
0.5–0.7), ordering imaging (OR: 1.4 CI: 1.2–1.7),
Table 3 Ten most frequent medical issues for
which opioids were prescribed
Medical issues
(n 5 2675)
Frequency
% (95% CI)
Back complaint
Prescription: all
Arthritis: all
Pain: chronic
Fracture
Sprain/strain
Migraine
Pain: post-op
Pain: shoulder
Pain: knee
609
219
198
172
119
74
58
50
42
40
22.77 (21.19, 24.40)
8.19 (7.18, 9.29)
7.4 (6.44, 8.46)
6.43 (5.53, 7.43)
4.45 (3.70, 5.30)
2.77 (2.18, 3.46)
2.17 (1.65, 2.79)
1.87 (1.39, 2.46)
1.57 (1.13, 2.12)
1.5 (1.07, 2.03)
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Medication name
accounting for 2932 (4.3%) of all prescriptions. One,
two or three opioids were provided in 91.1%, 8.5% and
0.5% of MIMWOs respectively. Specific opioids prescribed are presented in Table 2. Excluded from the
statistical analysis were 38 OST scripts as were the
18,693 problems for those aged under 16 years which
included 42 (0.22%) MIMWOs.
Pattern of Opioid Management
Table 4
Univariate associations of independent variables with opioid prescriptions
Opioid Prescribed
Variable
Patient Related Variables
Age group
Aboriginal and Torres Strait Islander
Non-English speaking background
Patient/practice status
Trainee related variables
Trainee gender
Qualified as doctor in Australia
Registrar age
Trainee working week
Training term
Worked at the practice previously
Practice related variables
Practice size
Practice routinely bulk bills
Rurality
Regional training provider
SEIFA
Consultation-related variables
Consultation Duration
Number of medical issues
New problem
“Chronic” condition
Sought help from any source
Imaging ordered
No (n 5 110215)
Yes (n 5 2675)
P
16–34
35–64
651
Male
Female
No
Yes
No
Yes
Returning patient
New to registrar
New to practice
31552 (98.5%)
51828 (97.3%)
24726 (97.3%)
37655 (97.2%)
69551 (97.9%)
103467 (97.7%)
1131 (95.0%)
98361 (97.6%)
6889 (98.2%)
49957 (97.3%)
50059 (97.9%)
7013 (98.3%)
486 (1.5%)
1464 (2.7%)
688 (2.7%)
1099 (2.8%)
1504 (2.1%)
2477 (2.3%)
60 (5.0%)
2424 (2.4%)
126 (1.8%)
1380 (2.7%)
1091 (2.1%)
123 (1.7%)
<0.0001
Male
Female
No
Yes
mean (SD)
Part time
Full time
Term 1
Term 2
Term 3
Term 4
No
Yes
36206 (97.1%)
74009 (97.9%)
26303 (97.3%)
82157 (97.7%)
33 (6.7)
23975 (97.8%)
83742 (97.6%)
44221 (97.8%)
36684 (97.5%)
23678 (97.6%)
5632 (97.5%)
76764 (97.8%)
31951 (97.4%)
1096 (2.9%)
1579 (2.1%)
733 (2.7%)
1893 (2.3%)
33 (6.7)
531 (2.2%)
2063 (2.4%)
995 (2.2%)
959 (2.5%)
575 (2.4%)
146 (2.5%)
1762 (2.2%)
868 (2.6%)
Small
Large
No
Yes
Major city
Inner regional
Outer regional or Remote
1
2
3
4
mean (SD)
36312 (97.6%)
71609 (97.7%)
90876 (97.6%)
18420 (97.6%)
63730 (97.9%)
32714 (97.4%)
13771 (96.7%)
40305 (97.5%)
13047 (97.0%)
11096 (97.5%)
45767 (97.9%)
5.3 (2.8)
903 (2.4%)
1697 (2.3%)
2204 (2.4%)
451 (2.4%)
1343 (2.1%)
859 (2.6%)
473 (3.3%)
1019 (2.5%)
405 (3.0%)
288 (2.5%)
963 (2.1%)
4.9 (2.8)
mean (SD)
mean (SD)
No
Yes
No
Yes
No
Yes
No
Yes
19 (9.9)
2.1 (1.0)
57443 (96.8%)
52772 (98.5%)
82480 (97.5%)
27385 (97.9%)
93860 (97.7%)
16355 (97.4%)
101634 (97.7%)
8581 (96.3%)
19 (11)
1.7 (0.9)
1897 (3.2%)
778 (1.5%)
2095 (2.5%)
575 (2.1%)
2243 (2.3%)
432 (2.6%)
2347 (2.3%)
328 (3.7%)
<0.0001
<0.0001
0.0156
<0.0001
<0.0001
0.0113
0.0986
0.1814
0.0447
0.0146
0.6857
0.8739
<0.0001
0.0004
0.0006
0.0912
<0.0001
<0.0001
<0.0001
0.0153
<0.0001
1725
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Patient gender
Class
Holliday et al.
Table 4
Continued
Opioid Prescribed
Variable
Class
No (n 5 110215)
Yes (n 5 2675)
P
Follow-up ordered
No
Yes
No
Yes
No
Yes
mean (SD)
60385 (98.1%)
49830 (97.1%)
94771 (97.7%)
15444 (97.5%)
96774 (97.8%)
13441 (96.3%)
0.6 (1.6)
1191 (1.9%)
1484 (2.9%)
2280 (2.3%)
395 (2.5%)
2154 (2.2%)
521 (3.7%)
0.2 (0.9)
<0.0001
Learning goals
Number pathology
<0.0001
<0.0001
* Numbers in columns may not add up to n because of missing data.
number of pathology tests ordered in the consultation
(OR: 0.7 for each one ordered, CI: 0.67–0.79), rates of
follow-up (OR: 1.5 CI: 1.4–1.7) and referral (OR: 1.4 CI:
1.2–1.5), consultation duration (by a statistically significant but clinically small amount) (OR: 1.02 CI: 1.017–
1.027), and number of other diagnoses (OR: 0.6 for
each extra diagnosis managed CI: 0.55–0.63).
The frequency of missing data was 5% for Aboriginal
and Torres Strait Islander status, with lower frequencies
for all other items justifying a complete-case analysis.
In terms of addiction-risk mitigation strategies, no
trainee reported contacting a Prescription Monitoring
Program and only two MIMWOs involved urine drug
screens. There were no reports of trainees seeking
authorization to comply with the various State regulations [5].
Initial Prescribing
Of opioids prescribed, 37.3% were “initial” prescriptions.
The univariate and multivariate associations of an “initial”
opioid prescription are presented in Supporting Information Tables 6 and 7. Most associations in the adjusted
model remained as for the general prescribing analysis.
Differences included associations losing statistical significance: patient gender, rurality, classification as
“chronic,” and SEIFA index. One association became
statistically significant with more senior trainees (Term 2
versus Term 1) more likely to prescribe opioids (OR:
1.21 C.I. 1.02–1.44). There were two associations which
were of opposite direction to general opioid prescribing
and reached statistical significance. “Initial” opioids were
more likely to be prescribed for a new problem rather
than a pre-existing one (OR 1.17 C.I 1.01–1.36) and to
a patient new to the registrar (OR 1.25 C.I 1.07–1.45).
Discussion
Overall Findings: Rates and Characteristics of
Prescription
Our findings demonstrate GP trainees provided opioids
predominantly as recurrent prescriptions for PNCP. How1726
ever, the frequency of MIMWOs (2.4%), was a lower rate
than in an Australian national study of established GPs
(3.7%) [18]. This could possibly be a response to recent
evidence indicating the relative benefits to harms of opioids
in PNCP have been over-stated [12,19,21,40]. However,
we found scant evidence trainees utilized any addiction-risk
mitigation strategies as recommended in guidelines such
as urinary drug screening, checking with a prescription
monitoring scheme, or seeking authorization as per each
state’s regulations [21,30,32]. Additionally there were 15
instances of opioid analgesic prescribing for dependency
indications.
Malignancy accounted for only 2.6% of MIMWOs in our
study (compared to established GPs: 3.5% opioids prescribed [18]). The most frequent specific indication was
for back complaints (22.8% MIMWOs versus more
established GPs 27.1% opioids prescribed [18]). There
is evidence clinicians are increasingly preferring opioid
analgesics over nonopioids for back pain [41] despite a
recent meta-analysis showing their long-term efficacy is
unclear with rates of concurrent substance use disorders and aberrant opioid behaviors as high as 43% and
24%, respectively [40].
As with other studies [18,29], codeine was most frequently
prescribed and tramadol the third most. Both rely on the
CYP2D6 enzyme to convert into morphine or become
active [42]. Because many people may have either ultrarapid or nonexistent rates of CYP2D6 activity, providing a
specific dose of either of these may equate to prescribing
an unknown dose of morphine [42]. While low-dose
codeine compounds may be purchased over-the-counter
from pharmacists unmonitored by regulators [30], uncompounded codeine is a “Schedule 8” or a controlled prescription medicine. Between 5% and 20% of all prescribed
codeine preparations have been found to be provided to
patients identified as “prescription shoppers” [26]. Recent
Australian entrants to OST for pharmaceutical dependency
identify their primary drug of concern as oxycodone (51%)
and codeine (28%) [43]. The second most commonly prescribed opioid, oxycodone has a relatively high abuse liability profile due to higher subjective attractiveness ratings
and a lack of negative subjective effects [44]. Increased
oxycodone prescribing is associated with opioid-related
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Referral ordered
0.4024
Pattern of Opioid Management
Table 5
Logistic regression model of associations of opioid prescriptions
Univariate
Adjusted
Class
OR (95% CI)
P
OR (95% CI)
P
Age group
Referent: 16–34
Patient gender
Aboriginal and Torres Strait Islander
Non English speaking background
Trainee gender
Trainee age
Training term
Referent: term 1
35–64
651
Male
Yes
Yes
Male
1.79
1.70
1.30
2.12
0.79
1.42
1.01
1.16
1.11
1.17
0.84
1.14
1.20
1.61
(1.61,
(1.49,
(1.19,
(1.63,
(0.65,
(1.26,
(1.00,
(1.04,
(0.97,
(0.95,
(0.73,
(1.03,
(1.06,
(1.37,
1.99)
1.94)
1.42)
2.74)
0.96)
1.60)
1.01)
1.29)
1.26)
1.44)
0.96)
1.26)
1.35)
1.89)
<0.0001
<0.0001
<0.0001
<0.0001
0.0156
<0.0001
0.0986
0.0068
0.1364
0.1406
0.0113
0.0146
0.0033
<0.0001
1.90
1.80
1.19
2.19
0.84
1.18
0.99
1.11
1.06
1.16
0.95
1.05
1.14
1.34
(1.68,
(1.56,
(1.09,
(1.63,
(0.67,
(1.03,
(0.98,
(0.97,
(0.91,
(0.89,
(0.81,
(0.92,
(0.96,
(1.07,
2.15)
2.08)
1.31)
2.94)
1.06)
1.36)
1.00)
1.27)
1.24)
1.52)
1.11)
1.20)
1.36)
1.69)
<0.0001
<.0001
0.0002
<0.0001
0.1514
0.0191
0.2311
0.1379
0.4464
0.2729
0.5381
0.4432
0.1438
0.0116
1.24
1.02
0.84
0.97
1.00
0.61
0.45
0.80
1.66
1.53
1.74
1.14
0.74
(1.03,
(0.84,
(0.74,
(0.95,
(1.00,
(0.58,
(0.41,
(0.72,
(1.46,
(1.41,
(1.58,
(1.03,
(0.69,
1.50)
1.24)
0.97)
0.99)
1.01)
0.65)
0.50)
0.89)
1.88)
1.67)
1.92)
1.27)
0.79)
0.0263
0.8601
0.0138
0.0006
0.0912
<0 .0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0153
<0.0001
1.11
0.88
0.95
0.97
1.02
0.59
0.40
0.60
1.43
1.54
1.37
0.95
0.73
(0.88,
(0.69,
(0.79,
(0.95,
(1.02,
(0.55,
(0.36,
(0.53,
(1.24,
(1.38,
(1.22,
(0.83,
(0.67,
1.41)
1.11)
1.14)
1.00)
1.03)
0.63)
0.44)
0.68)
1.65)
1.71)
1.54)
1.08)
0.79)
0.3914
0.2835
0.5980
0.0319
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.4135
<0.0001
Qualified as doctor in Australia
Worked at the practice previously
Rurality
Referent: Major city
Regional training provider
Referent: 1
SEIFA
Consultation duration
Number of medical issues
New problem
“Chronic” condition
Imaging ordered
Follow-up ordered
Referral ordered
Sought help from any source
Number pathology
Term 2
Term 3
Term 4
Yes
Yes
Inner regional
Outer regional/
Remote/Very
remote
2
3
4
Yes
Yes
Yes
Yes
Yes
Yes
mortality [10] leading to increased restrictions in some jurisdictions [44].
Associations of Overall Prescribing (Including Both
Initial and Continuing Prescriptions)
The association of opioid prescribing with certain patient,
prescriber and practice demographics in our study may be
of concern. A systematic review found higher levels of
opioid prescribing related to opioid-related harms including
mortality [10]. A US analysis showed patient gender and
race reliably influenced pain management decisions of
almost half (45%) of US medical trainees [45]. Variations in
opioid prescribing rates, in another US study, were found
to relate more strongly to physician behaviors than either
shifting demographics or disease incidence rates [20]. The
importance of these findings is that rates of opioid prescribing are directly related to adverse outcomes.
Associations of increased trainee prescribing included
patient older age, male gender, Aboriginal/Torres Strait
Islander status, more rural location and lower socioeconomic location. These associations paralleled the demographics over-represented in OST programs [5],
amongst opioid misusers [26,46], and increased opioidrelated mortality [10]. The aged are particularly at risk of
opioid-related toxicities such as falls, respiratory depression, delirium and sedation [15,23,24] . The association
with older age in our study may have been more prominent if consultations in residential aged care facilities
had been included; with an estimated 28.1% residents
there taking regular opioids [23]. Our finding of males
being prescribed more opioids has been shown in
some previous studies [18,20,47]. It is said to reflect
prescribers’ subjective judgements due to social categorization [47]. We found that Aboriginal and Torres
Strait Islander patients received more opioids. This may
reflect a greater symptom burden [11]. Alternatively, US
studies indicate race may influence analgesia decisions
[3,45,48] especially those made by inexperienced doctors under cognitive stress [47]. But increased prescribing for Aboriginal and Torres Strait Islander patients may
reflect increased opioid prescribing to lower
1727
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Variable
Holliday et al.
socioeconomic and more rural populations [29]. In the
US, these latter groups have higher rates of selfreported pain, and higher rates of opioid analgesic misuse [46]. Such treatment disparities may reflect lack of
access to health services of these groups and the relative expense and geographical inaccessibility of nonopioid or multidisciplinary pain management [11,21,26,46].
The analysis indicates these “uninitiated” prescribers
found MIMWOs problematic. The increased recourse to
imaging may reflect attempts to ascertain the biophysical basis of the problem to judge the legitimacy of the
patient’s pain status or the opioid use [3,14]. The complex needs of patients on opioid analgesics are further
indicated by increased rates of referral and follow-up
and increased consultation duration despite a
decreased number of medical issues addressed per
consultation. Increased duration of consultations involving opioids has been found elsewhere, particularly when
a pain patient is new to a doctor [52].
Differences in Associations of Overall Prescription
versus “Initial” Prescription
An “initial” opioid script was more likely to be provided
to patients not seen previously by the trainee, and to
patients with new problems, some of whom may be
“prescription shoppers.” This suggests that for these
new pain presentations that trainees may not first trial
nonopioid or nonpharmacologic analgesia prior to the
initiation of opioids, which is inconsistent with guidelines
1728
Strengths and Limitations
Strengths of the study include a large sample size from
four states across all rural–urban classifications with a
response rate singularly high for a GP consultation-level
study [31] and with participant demographics broadly
similar to those of Australian GP trainees overall [53].
The contemporaneous recording of detailed patient,
prescriber, practice and consultation variables and the
diagnostic indication for prescription and whether this
diagnostic indication was a new medical issue, all linked
to the individual prescription is a particular strength. As
is our ability to ascertain all prescribed and recommended opioid medications, not only those that attract
a PBS subsidy.
Limitations of this analysis include its cross-sectional
“consultation snapshot” nature and lack of data on full
medication regimens. Also, as a secondary analysis of
an existing data set, we do not have data on some variables of particular relevance to this research question:
patient mental health status, substance use history pain
scores or dosages. This means our analysis does not
allow assessment of appropriateness or quality of trainees’ prescribing decisions, and could allow that any
associations of opioid prescription were associations of
patient pain per se.
Implications for Practice and Policy
The demographic (rather than strictly clinical) associations
of opioid prescribing in our study raise important issues
regarding opioid and pain management. Being older,
male, or Aboriginal/Torres Strait Islander, and being from
rural or remote and lower socioeconomic status locations
were all associated with opioid prescription in adjusted
models. These groups may thus be at disproportionate
risk of poor pain management or opioid-related harms.
High rates of prescribing are important determinants of
rates of fatal overdose [10] and iatrogenic opioid dependency [54] and correlate with demographics overrepresented on OST programs [5].
Our findings should inform educational interventions
addressing barriers to and promoting guideline consistent opioid and pain management [13,14,54]. Our findings also indicate the need for more accessible nonopioid pain management services, especially for geographically isolated patients and socially marginalized
groups.
Future Research
Prescribing habits start early on in a clinical career, so
improving QUM starts with the education of those in
training. Increasing awareness may reduce stigma and
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Opioids were mainly prescribed as repeat scripts for preexisting medical issues to patients familiar to the practice. A review of the reasons clinicians fail to deprescribe
identified four reasons why trainees may default to careas-usual [49]: nonidentification of the medication as inappropriate; an inertia where continuing to prescribe seems
less trouble (e.g., from withdrawal syndromes); hesitance
about the management of pain (and its pharmaceuticals);
and external factors example, time pressures and patient
resistance [2,3,49]. Those on opioid maintenance for
analgesia describe poorer physical function and higher
psychological distress [29] and so trainees may feel intrapractice consensus is required prior to initiating a change
in management. But deprescribing is difficult for most
GPs with 89%, in an Australian survey, reporting never
or only “occasionally” terminating opioids even when
faced with aberrant opioid behaviors [13]. In a large US
survey one year after the commencement of long-term
opioid analgesia, only 7.5% patients had discontinued
them and only 20% had done so when followed for up
to 3.5 years [50]. The potential for adverse effects in continued prescribing is highlighted in another US study
which showed that duration of opioid analgesia prescribing is strongly associated with risk of opioid use disorders [51]. Amongst Australian entrants to OST for
pharmaceutical dependency, two thirds (66%) had initiated opioids for analgesia and 28% continued them for
this reason [43].
[17,21,24]. These findings suggest “independent”
trainee opioid prescribing decisions for new presentations are made similarly to those made for “inherited”
PNCP cases.
Pattern of Opioid Management
unintentional bias [48] and reduce discomfort managing
the complex and competing demands of pain and
addiction management [1,8]. The trainee cohort study
described in this paper provides a unique opportunity to
trial an intervention in an educationally receptive cohort
to promote multimodal pain management [34].
Conclusion
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Table 6 Univariate associations of independent variables
with initial opioid prescriptions
Table 7 Logistic regression model of associations of initial opioid prescriptions
1731
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