Prevalence and predictors of opioid use before orthopaedic surgery in an Australian setting: A multicentre,cross-sectional,observational study

Abstract

Opioid analgesics are commonly used by patients awaiting orthopaedic surgery, and preoperative opioid use is associated with a greater burden of postoperative pain, suboptimal surgical outcomes and higher healthcare costs. This study aimed to examine the prevalence of total opioid use before elective orthopaedic surgery with a focus on regional and rural hospitals in New South Wales, Australia. This was a cross-sectional, observational study of patients undergoing orthopaedic surgery conducted between April 2017 and November 2019 across five hospitals that included a mix of metropolitan, regional, rural, private and public settings. Preoperative patient demographics, pain scores and analgesic use were collected during pre-admission clinic visits, held between two and six weeks before surgery. Of the 430 patients included, 229 (53.3%) were women and the mean age was 67.5 (standard deviation 10.1) years. The overall prevalence of total preoperative opioid use was 37.7% (162/430). Rates of preoperative opioid use ranged from 20.6% (13/63) at a metropolitan hospital to 48.8% (21/43) at an inner regional hospital. Multivariable logistic regression showed that the inner regional setting was a significant predictor of opioid use before orthopaedic surgery (adjusted odds ratio 2.6; 95% confidence interval 1.0 to 6.7) after adjusting for covariates. Opioid use prior to orthopaedic surgery is common and appears to vary by geographical location.

Keywords

Opioids preoperative opioid use orthopaedic surgery

Introduction

Opioids are cornerstone agents for the management of moderate to severe acute pain, but their role in chronic non-cancer pain is limited by opioid-related adverse effects and tolerance leading to reduced pain relief.¹ Opioids are often prescribed for osteoarthritis, including those awaiting orthopaedic surgery. In Australia, up to 54% of patients are prescribed opioid analgesics before hip and knee replacement surgery.² Opioid use before surgery has been associated with a greater burden of postoperative pain,³ suboptimal surgical outcomes,⁴ longer length of hospital stay and higher healthcare costs.^5,6 Furthermore, preoperative opioid use has been identified as a key predictor of long-term opioid use after surgery.⁷ Harms associated with long-term opioid use include sedation, constipation, increased falls risk, respiratory depression, tolerance and dependence.^8,9 Thus, examining the prevalence and predictors of preoperative opioid use may aid clinicians in identifying patients at an increased risk of harm.

One such predictor has been highlighted in studies from the USA involving patients undergoing orthopaedic surgery. These studies demonstrate a link between increased preoperative opioid use and areas of socioeconomic disadvantage.^10,11 In Australia, significant variation by geographical location in rates of opioid use was reported in the 2015 Australian Atlas of Healthcare Variation, which highlighted a more than tenfold difference between the regions with the highest rate and lowest rate of opioid prescriptions dispensed under the Pharmaceutical Benefits Scheme.¹² These variations were predominantly observed when comparing metropolitan with regional or rural areas where access to healthcare resources may be scarce and average socioeconomic status was lower.¹²

Other predictors of preoperative opioid use were described in a 2018 cross-sectional study of 34,186 patients undergoing a broad range of surgeries at a tertiary academic medical centre in the USA, and included current tobacco use (adjusted odds ratio (aOR) 1.62; 95% confidence interval (CI) 1.48 to 1.78), illicit drug use (aOR 1.74; 95% CI 1.16 to 2.60), higher pain severity (aOR 1.33; 95% CI 1.31 to 1.35), depression (aOR 1.22; 95% CI 1.12 to 1.33), and multiple medical comorbidities (aOR 1.47; 95% CI 1.37 to 1.58).¹³ There is limited research conducted to examine the patterns of preoperative opioid use across geographical locations in the Australian context. Therefore, the primary aim of this study was to examine the prevalence of total opioid use before elective orthopaedic surgery across Australian metropolitan, regional and rural locations. The secondary aim of this study was to examine the association between hospital location and total preoperative opioid use while adjusting for covariates.

Materials and methods

Study design

This was a multicentre, cross-sectional, observational study of patients undergoing elective orthopaedic surgery at five hospitals in New South Wales, Australia:

A 480-bed metropolitan private hospital

A 796-bed inner regional public hospital

A 292-bed outer regional public hospital (outer regional hospital A)

A 151-bed outer regional private hospital (outer regional hospital B)

A 180-bed rural public hospital

The study was conducted between April 2017 and November 2019. It was approved by the St Vincent’s Hospital Human Research Ethics Committee (LNR/17/SVH/155) and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹⁴

Study population

We included patients aged 18 years or older who were scheduled to undergo elective orthopaedic surgery. Exclusion criteria included surgery due to malignancy, surgical procedures completed under local anaesthetic only, patients using opioid formulations for opioid use disorder or antitussive purposes, and inability to complete the study questionnaire due to a medical condition (such as dementia) or significant language barrier. Simple randomisation was used to enrol a sample of eligible patients during a hospital pre-admission visit held between two and six weeks before surgery. Written informed consent was obtained at study enrolment.

Data collection

At the pre-admission clinic visit, participants were asked by study investigators or pre-admission clinic staff to complete a 26-item questionnaire to collect demographic and clinical information, including age, sex, analgesic use, average pain intensity over the previous 24 hours, residential postcode, identification as an Aboriginal or Torres Strait Islander, orthopaedic procedure conducted, highest education level, employment status and relevant psychological and social background. Orthopaedic procedures classified as ‘other’ included Achilles tendon repair, ankle replacement, removal of clavicle plate, revision for total knee replacement, revision for total hip replacement and unilateral metatarsal replacement. Patients were not offered incentives to participate in the study.

Opioid use was defined as the use of buprenorphine, hydromorphone, fentanyl, oxycodone, tramadol, tapentadol, codeine, morphine, or methadone as these were the opioids available at the time in Australia.

Opioid use was divided into ‘daily use’ or ‘total use’. Daily opioid use was defined as opioid use every day over the previous two weeks or more. Total opioid use included daily opioid use in addition to when-required use over the previous two weeks or more. Daily oral morphine milligram equivalents (MMEs) were calculated using established conversion factors.¹⁵ Additional doses taken on a ‘when-required’ basis were accounted for by determining the average use over the previous two week period and calculating a daily oral MME quantity.

Non-opioid analgesia included paracetamol, non-steroidal anti-inflammatory agents, and gabapentinoids, including gabapentin and pregabalin.

Average pain intensity during the 24 hours prior to questionnaire administration was collected using an 11-point numerical pain rating scale, a validated and responsive tool for the measurement of pain intensity.¹⁶ A score of 0 indicated no pain and 10 indicated the worst imaginable pain.

Participant residential postcode was used to calculate remoteness classification and socioeconomic decile. Remoteness was classified using the Australian Statistical Geography Standard – Remoteness Area (ASGS-RA).¹⁷ Values ranged from 1 to 6, in which 1 indicated a major city of Australia, 2 indicated inner regional Australia, 3 indicated outer regional Australia, 4 indicated remote Australia, 5 indicated very remote Australia, and 6 indicated a migratory (offshore) region.

In addition to the ASGS-RA classification, we distinguished the metropolitan hospital location apart from regional and rural locations using previous Australian reports of opioid-related harm.^18,19

Socioeconomic decile was collected using the Index of Relative Socio-economic Disadvantage (IRSD), an indicator developed by the Australian Bureau of Statistics which summarises several variables including low income, low educational attainment, high unemployment, and unskilled occupations by postal area.²⁰ Values ranged from 1 to 10, in which decile 1 indicated the greatest disadvantage and decile 10 indicated the least disadvantage.

Psychological and social background data were collected using validated tools including the two-item Patient Health Questionnaire (PHQ-2) to collect depressive traits,²¹ the two-item Generalised Anxiety Disorder Questionnaire (GAD-2) to collect anxiety traits,²² and the five-item Screener and Opioid Assessment for Patients with Pain (SOAPP-R version 1.0-SF) tool to identify persons at risk of opioid use disorder.²³

Sample size

We calculated the sample size required to obtain a representative population of patients attending the five study hospitals using methodology described by Krejcie and Morgan.²⁴ Using a degree of accuracy set to 0.05 to ensure a margin of error less than 5%, the confidence coefficient set to 3.84 to allow a 95% CI for reporting the margin of error, and population proportion value set to 0.5, the required sample size was calculated at 384 patients. To account for a potential dropout rate of 10% based on previous literature,²⁵ we aimed to recruit 423 participants.

Data analysis

Univariate analyses were conducted to determine differences between opioid naïve participants and those using opioids before surgery. The chi-square test was used to compare categorical variables. The Fisher’s exact test was used when over 20% of cells contained an expected frequency of less than five. Differences between non-normally distributed continuous variables were compared using the Mann–Whitney U test.

Multivariable logistic regression was conducted to examine whether hospital location was a predictor of total preoperative opioid use while adjusting for known covariates: female sex, younger age,¹⁰ low education level, low socioeconomic decile,^10,11 history of chronic pain (at any area of the body), depression, and tobacco use.¹³ Depression traits used in the logistic regression model referred to scoring 3 or 4 for feeling little interest, down, depressed, hopeless, or feeling little pleasure on a 4-point Likert scale, in which a score of 0 indicated ‘not at all’ and a score of 4 indicated ‘nearly every day’. A sensitivity analysis was performed for the regression model and socioeconomic decile was removed to account for collinearity with hospital setting. Pain score in the previous 24 hours, percentage relief from pain medications, and non-opioid analgesia use was also removed to account for collinearity with a history of chronic pain.

We planned to manage any missing data using multiple imputation.

All statistical analyses were performed using IBM SPSS Statistics version 27 (IBM, Armonk, NY, USA). A P < 0.05 was considered statistically significant.

Results

There were 506 participants invited to participate in the study, of whom 76 declined participation and 430 participants were recruited. The mean age was 67.5 (standard deviation (SD) 10.1) years and 229 (53.3%) were women. Further participant demographics are reported in Tables 1 and 2. The three most common orthopaedic procedures conducted in this cohort were unilateral total knee replacement (55.6%, 239/430), unilateral total hip replacement (29.1%, 125/430) and unilateral total shoulder replacement (5.1%, 22/430) (Table 1). Rates of orthopaedic procedures conducted at individual hospital sites are reported in Supplementary Table 1.

Table 1.

Characteristics of patients awaiting elective orthopaedic surgery.

Demographics	n (N = 430)
Age, years, mean (SD)	67.5 (10.1)
Sex, female, n (%)	229 (53.3%)
Hospital geographical location, n (%)
Metropolitan hospital	63 (14.7%)
Inner regional hospital	43 (10%)
Outer regional hospital A	132 (30.7%)
Outer regional hospital B	82 (19.1%)
Rural hospital	110 (25.6%)
Remoteness (ASGS-RA), n (%)
1	122 (28.4%)
2	258 (60%)
3	50 (11.6%)
Socioeconomic decile (IRSD), n (%)
1	118 (27.4%)
2	96 (22.3%)
3	50 (11.6%)
4	28 (6.5%)
5	24 (5.6%)
6	17 (4%)
7	25 (5.8%)
8	21 (4.9%)
9	25 (5.8%)
10	26 (6%)
Identification as Aboriginal or Torres Strait Islander, n (%)
Aboriginal	13 (3%)
Torres Strait Islander	0 (0%)
Orthopaedic procedure by type, n (%)
Unilateral total knee replacement	239 (55.6%)
Unilateral total hip replacement	125 (29.1%)
Unilateral total shoulder replacement	22 (5.1%)
Bilateral knee replacement	9 (2.1%)
ACL reconstruction surgery	3 (0.7%)
Bilateral hip replacement	3 (0.7%)
Partial knee replacement	2 (0.5%)
Unilateral shoulder repair or reconstruction	2 (0.5%)
Other	25 (5.8%)
Highest education level, n (%)
Grade 10 or below	226 (52.6%)
Grade 12	61 (14.2%)
University or TAFE	132 (30.7%)
Employment status, n (%)
Unemployed	22 (5.1%)
Employed	84 (19.5%)
Retired	265 (61.6%)
Student	1 (0.2%)
Unable to work due to disability	35 (8.1%)
Other	15 (3.5%)

SD: standard deviation; ASGS-RA: Australian Statistical Geography Standard – Remoteness Area; IRSD: Index of Relative Socio-economic Disadvantage; ACL: anterior cruciate ligament; TAFE: technical and further education.

Table 2.

Psychological and lifestyle background of patients awaiting elective orthopaedic surgery.

Demographics	n (N = 430)
Psychological background, mean (SD)
Feeling nervous, anxious or on edge^a	1.5 (0.9)
Worrying^a	1.3 (0.7)
Feeling little interest or pleasure^a	1.5 (0.9)
Feeling down, depressed or hopeless^a	1.4 (0.8)
Experiencing mood swings^b	2.1 (1)
Other,^b mean (SD)
Smoking a cigarette within an hour of waking up	1.3 (0.9)
Medications taken other than prescribed	1.2 (0.6)
Recreational drug use in past five years	1.1 (0.4)
Legal problems or arrested	1.1 (0.4)
At risk for opioid use disorder	1.1 (0.5)

SD: standard deviation.

^aUsing 4-point Likert scale (0 = not at all, 4 = nearly every day).

^bUsing 5-point Likert scale (0 = never, 5 = very often).

The majority of participants resided in a remoteness classification 2 area (60%, 258/430) or remoteness classification 1 area (28.4%, 122/430). Most participants resided in areas of socioeconomic decile 1 (27.4%, 118/430), 2 (22.3%, 96/430) or 3 (11.6%, 50/430) (Table 1).

A history of chronic pain was reported by 323 participants (75.1%) and the mean pain score reported in the past 24 hours was 5.3 (SD 2.7). The primary outcome, preoperative opioid use, was reported in 162 participants (37.7%) and 108 participants (25.1%) reported daily opioid use in the past two weeks (Table 3). Rates of total preoperative opioid use among participants admitted to the metropolitan hospital and inner regional hospital were 20.6% (13/63) and 48.8% (21/43), respectively, 40.2% at outer regional hospital A (53/132) and outer regional hospital B (33/82) and 38.2% (42/110) at the rural hospital (Supplementary Table 2). Rates of total preoperative opioid use prior to individual procedure types are reported in Supplementary Table 3. The most frequently used opioids were codeine (21.4%, 92/430), oxycodone (11.4%, 49/430) and tramadol (5.1%, 22/430). The overall median MME per day was 15 (interquartile range (IQR) 23.7 (2–300)) (Table 3).

Table 3.

Pain experience and analgesics used by patients awaiting elective orthopaedic surgery.

Pain experience and analgesia use	n (N = 430)
Pain experience
History of chronic pain, n (%)	323 (75.1%)
Pain score in last 24 hours,^a mean (SD)	5.3 (2.7)
Percentage relief from pain medications, mean (SD)	40.2 (29.5)
Pain experienced on most days, n (%)	350 (81.4%)
Pain experienced at surgery site, n (%)	339 (78.8%)
Pain experienced in two or more sites, n (%)	220 (51.2%)
Preoperative analgesia use
Non-opioid analgesia use, n (%)	84 (19.5%)
Daily opioid analgesia use, n (%)	108 (25.1%)
Total opioid analgesia use, n (%)	162 (37.7%)
Opioid use by type, n (%)
Codeine	92 (21.4%)
Oxycodone	49 (11.4%)
Morphine	7 (1.6%)
Tramadol	22 (5.1%)
Tapentadol	12 (2.8%)
Buprenorphine	9 (2.1%)
Fentanyl	10 (2.3%)
Total oral morphine milligram equivalents, median (IQR)	15 (23.7 (2–300))

SD: standard deviation; IQR: interquartile range.

^aUsing 11-point numerical pain rating scale (0 = no pain, 10 = worst imaginable pain).

Univariate differences between opioid naïve participants and those using opioids preoperatively are reported in Table 4. There were significantly more women in the preoperative opioid use cohort compared with the opioid naïve group (61.1%, 99/162 versus 48.5%, 130/268; P = 0.013).

Table 4.

Characteristics of patients awaiting elective orthopaedic surgery.

Demographics	Not using opioids preoperatively (n = 268)	Using opioids preoperatively (n = 162)	P-value
Age, years, mean (SD)	67.6 (10.3)	67.3 (9.8)	0.809
Sex, female, n (%)	130 (48.5%)	99 (61.1%)	0.013*
Hospital geographical location, n (%)
Metropolitan hospital	50 (18.7%)	13 (8%)	0.003*
Inner regional hospital	22 (8.2%)	21 (13%)	0.135
Outer regional hospital A	79 (29.5%)	53 (32.7%)	0.518
Outer regional hospital B	49 (18.3%)	33 (20.4%)	0.614
Rural hospital	68 (25.4%)	42 (25.9%)	0.91
Public or private hospital, n (%)
Public	169 (63.1%)	116 (71.6%)	0.074
Private	99 (36.9%)	46 (28.4%)	0.074
Remoteness (ASGS-RA), n (%)
1	79 (29.5%)	43 (26.5%)	0.581
2	159 (59.3%)	99 (61.1%)	0.761
3	30 (11.2%)	20 (12.3%)	0.757
Socioeconomic decile (IRSD), n (%)
1	67 (25%)	51 (31.5%)	0.149
2	59 (22%)	37 (22.8%)	0.905
3	29 (10.8%)	21 (13%)	0.536
4	17 (6.3%)	11 (6.8%)	0.843
5	16 (6%)	8 (4.9%)	0.829
6	15 (5.6%)	2 (1.2%)	0.038*
7	16 (6%)	9 (5.6%)	1
8	12 (4.5%)	9 (5.6%)	0.648
9	18 (6.7%)	7 (4.3%)	0.396
10	19 (7.1%)	7 (4.3%)	0.299
Identification as Aboriginal or Torres Strait Islander, n (%)
Aboriginal	9 (3.4%)	4 (2.5%)	0.774
Torres Strait Islander	0 (0%)	0 (0%)	N/A
Preoperative analgesia use, n (%)
Non-opioid analgesia use	33 (12.3%)	51 (31.5%)	<0.001*
Pain experience
History of chronic pain, n (%)	187 (69.8%)	136 (84%)	0.001*
Pain score in last 24 hours,^a mean (SD)	4.9 (2.8)	5.8 (2.4)	<0.001*
Percentage relief from pain medications, mean (SD)	34 (29)	50.4 (27.3)	<0.001*
Pain experienced on most days, n (%)	213 (79.5%)	137 (84.6%)	0.203
Pain experienced at surgery site, n (%)	204 (76.1%)	135 (83.3%)	0.088
Pain experienced in two or more sites, n (%)	131 (48.9%)	89 (54.9%)	0.234
Highest education level, n (%)
Grade 10 or below	133 (49.6%)	93 (57.4%)	0.135
Grade 12	41 (15.3%)	20 (12.3%)	0.476
University or TAFE	87 (32.5%)	45 (27.8%)	0.333
Employment status, n (%)
Unemployed	15 (5.6%)	7 (4.3%)	0.655
Employed	54 (20.1%)	30 (18.5%)	0.708
Retired	170 (63.4%)	95 (58.6%)	0.357
Student	1 (0.4%)	0 (0%)	N/A
Unable to work due to disability	16 (6%)	19 (11.7%)	0.044*
Other	7 (2.6%)	8 (4.9%)	0.277
Psychological background, mean (SD)
Feeling nervous, anxious or on edge^b	1.5 (0.9)	1.6 (0.9)	0.27
Worrying^b	1.3 (0.7)	1.4 (0.8)	0.146
Feeling little interest or pleasure^b	1.4 (0.8)	1.6 (1)	0.143
Feeling down, depressed or hopeless^b	1.4 (0.8)	1.5 (0.9)	0.083
Experiencing mood swings^c	2 (1)	2.2 (0.9)	0.025*
Other,^c mean (SD)
Smoking a cigarette within an hour of waking up	1.2 (0.8)	1.4 (1.1)	0.016*
Medications taken other than prescribed	1.2 (0.6)	1.3 (0.7)	0.291
Recreational drug use in past five years	1.1 (0.4)	1.1 (0.4)	0.825
Legal problems or arrested	1.1 (0.4)	1.1 (0.4)	0.703
At risk for opioid use disorder	1.1 (0.4)	1.2 (0.6)	0.259

SD: standard deviation; ASGS-RA: Australian Statistical Geography Standard – Remoteness Area; IRSD: Index of Relative Socio-economic Disadvantage; TAFE: technical and further education.

^aUsing 11-point numerical pain rating scale (0 = no pain, 10 = worst imaginable pain).

^bUsing 4-point Likert scale (0 = not at all, 4 = nearly every day).

^cUsing 5-point Likert scale (0 = never, 5 = very often).

*P-value using chi-square or Fisher’s exact test for categorical variables or Mann–Whitney U test for continuous variables less than 0.05.

Multivariable logistic regression showed participants undergoing surgery at an inner regional location were more likely to use opioids prior to orthopaedic surgery (aOR 2.6; 95% CI 1.0 to 6.7) compared with participants attending a metropolitan hospital. Covariates of opioid use before orthopaedic surgery included female sex (aOR 1.6; 95% CI 1.1 to 2.5), a history of chronic pain (aOR 1.9; 95% CI 1.1 to 3.3), depression traits (aOR 1.8; 95% CI 1.0 to 3.1), and tobacco use ‘very often’ (aOR 3.9; 95% CI 1.3 to 11.9). The Hosmer–Lemeshow test²⁶ indicated a good fit of the data to the logistic regression model (P > 0.05) (Table 5).

Table 5.

Multivariable logistic regression analyses for predictors and covariates of preoperative opioid use before elective orthopaedic surgery.

Variable	Adjusted OR (95% CI)^a
Hospital geographical location
Metropolitan hospital	(Reference)
Inner regional hospital	2.6 (1.0–6.7)*
Outer regional hospital A	1.6 (0.7–3.5)
Outer regional hospital B	2.2 (0.9–5.1)
Rural hospital	1.9 (0.9–4.2)
Age, years
18–30	(Reference)
31–40	3.6 (0.1–109.6)
41–50	2.3 (0.2–30)
51–60	1 (0.1–10.3)
61–70	1 (0.1–10.1)
71–80	1.4 (0.1–14.8)
≥81	0.9 (0.1–11.2)
Sex
Male	(Reference)
Female	1.6 (1.1–2.5)*
History of chronic pain
No	(Reference)
Yes	1.9 (1.1–3.3)*
Depression traits
No	(Reference)
Yes	1.8 (1.0–3.1)*
Education level
Grade 12 and above	(Reference)
Grade 10	1.1 (0.7–1.8)
Tobacco use^b
Tobacco use less than ‘very often’	(Reference)
Tobacco use ‘very often’	3.9 (1.3–11.9)*

OR: odds ratio; CI: confidence interval.

^aHosmer–Lemeshow goodness of fit (P = 0.815).

^bTobacco use within one hour of waking up rated using 5-point Likert scale (0 = never, 5 = very often).

*P-value using Wald test less than 0.05.

Discussion

In this study, we identified that 37.7% (162/430) of patients used opioids preoperatively, with significant variation between metropolitan and regional locations. This is within the range of rates of preoperative opioid use previously reported in other Australian orthopaedic populations. These studies reported rates of preoperative opioid use between 15.7% of patients attending a metropolitan hospital using opioids daily²⁷ and 54% of older patients using data from the Australian Government Department of Veterans’ Affairs reporting total opioid use.² The relatively higher prevalence of preoperative opioid use identified in our cohort may partly be explained by the reporting of total opioid use rather than daily opioid use. Our results are consistent with data from the 2015 Australian Atlas of Healthcare Variation highlighting the disparity in rates of overall opioid use between metropolitan and regional or rural areas.¹² In addition, our study demonstrated that these trends appeared to extend to preoperative opioid use in Australia.

A novel and unexpected finding was the identification of the inner regional hospital location as a significant predictor of preoperative opioid use, after accounting for known covariates. Potential factors accounting for the rates of higher preoperative opioid use outside capital city areas include local prescribing factors, inadequate access to health services, and a greater prevalence of chronic pain, depression and tobacco use among these populations.^10,13 Interestingly, while patients receiving surgery at a rural hospital location reported higher rates of preoperative opioid use compared with patients attending a metropolitan hospital, the rural location alone was not identified as a significant predictor of preoperative opioid use. Thus, further research should determine whether these trends are reproduced across other rural Australian settings. Once identified, strategies to address the prevalence and known predictors of preoperative opioid use across regional and rural areas of Australia are warranted.

The longer duration spent on public hospital waiting lists for orthopaedic surgery (such as joint replacement) compared with private hospital waiting lists has been proposed as one reason why preoperative opioid use is more prevalent among public hospital patients.²⁸ However, our study showed comparable rates of preoperative opioid use between the regional private and regional public hospitals. Thus, rates of opioid use before orthopaedic surgery appear to vary by location rather than private or public hospital designation in this population.

Guidelines by the Royal Australian College of General Practitioners advise the use of non-opioid analgesia such as non-steroidal anti-inflammatory drugs and paracetamol for the management of pain associated with hip or knee osteoarthritis.²⁹ However, our data highlighted particularly low rates of non-opioid analgesia use among patients awaiting hip or knee arthroplasty, with less than 20% of patients taking non-opioid analgesics among the total cohort and less than one in three patients reporting non-opioid analgesic use alongside preoperative opioid use. Therefore, this study highlights that non-opioid analgesic use for hip or knee osteoarthritis pain is an area requiring increased clinical attention to optimise patient outcomes.

Finally, a 2019 systematic review and meta-analysis concluded that opioids provided no clinically relevant reduction in pain or disability compared with placebo in chronic osteoarthritic hip or knee pain.³⁰ Despite this, our findings demonstrated a greater proportion of patients with significant disability among the preoperative opioid use cohort compared with opioid naïve patients before surgery. While the direction of cause and effect could not be determined due to the cross-sectional nature of this study, this correlation represents a key opportunity for improved healthcare practice by increasing the use of non-drug treatments to improve patient function and reducing opioid use for chronic osteoarthritis pain.²⁹

A key strength of this study is the inclusion of multiple hospitals across a geographical spectrum from a metropolitan location through to regional and rural locations, which improves the generalisability of our findings.

However, we acknowledge a number of limitations to our study. First, patient self-reported data were used, which carries a risk of response bias and recall bias. The prevalence of opioid dependency prior to surgery was unknown and may have been inflated by the criteria to determine daily opioid use. Patterns of analgesic use may have varied across the data collection period of two to six weeks prior to surgery. The measurement of tobacco use using a five-point Likert scale is less robust compared with a validated scale such as the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition.³¹ Apart from chronic pain, other comorbid conditions and indicators of overall comorbidity burden were not collected. The specific indication for opioid use was not collected, thus the prevalence of preoperative opioid use may be over-represented in this cohort. Also, our inclusion criteria included participants using opioids for two weeks or longer, which may overestimate those at risk of harm due to preoperative opioid use. The ASGS-RA classifications of remoteness used in the present study were not sensitive enough to capture the observed variation in rates of opioid use between metropolitan and regional locations. As such, we distinguished metropolitan locations from inner and outer regional locations. Alternative measures to determine remoteness classification beyond the ASGS-RA, such as the Modified Monash Model (which involves more classifications than the ASGS-RA, allowing greater sensitivity),³² should be considered when conducting future research in this area. While a trend towards a higher prevalence of preoperative opioid use was observed among the regional and rural settings in comparison to the metropolitan location, a statistically significant difference was reported for only one site. The observed difference may thus be unique to the hospital site or due to unmeasured factors such as local prescribing practices which could not be measured. Finally, given the included hospital sites were located within one state in Australia, the generalisability of our findings to different Australian states and territories may be limited.

In conclusion, nearly 40% of patients presenting for elective orthopaedic surgery were using some opioid medication prior to admission, with significant variation observed between metropolitan and regional or rural settings. Given that preoperative opioid use is associated with worse postoperative outcomes and that preoperative use varies by regional location, different strategies may be required to address opioid use prior to surgery based on the local context.

Supplemental Material

sj-pdf-1-aic-10.1177_0310057X221147066 - Supplemental material for Prevalence and predictors of opioid use before orthopaedic surgery in an Australian setting: A multicentre, cross-sectional, observational study

Supplemental material, sj-pdf-1-aic-10.1177_0310057X221147066 for Prevalence and predictors of opioid use before orthopaedic surgery in an Australian setting: A multicentre, cross-sectional, observational study by Shania Liu, Jennifer A Stevens, Ashleigh E Collins, Jed Duff, Joanna R Sutherland, Morgan D Oddie, Justine M Naylor, Asad E Patanwala, Benita M Suckling and Jonathan Penm in Anaesthesia and Intensive Care

Footnotes

Author Contribution(s)

Shania Liu: Formal analysis; Investigation; Methodology; Project administration; Writing – original draft; Writing – review & editing.

Jennifer A Stevens: Conceptualization; Investigation; Methodology; Project administration; Supervision; Writing – review & editing.

Ashleigh E Collins: Conceptualization; Data curation; Methodology; Writing – review & editing.

Jed Duff: Conceptualization; Methodology; Project administration; Writing – review & editing.

Joanna R Sutherland: Conceptualization; Methodology; Project administration; Writing – review & editing.

Morgan D Oddie: Data curation; Methodology; Project administration; Resources; Writing – review & editing.

Justine M Naylor: Supervision; Writing – review & editing;

Asad E Patanwala: Methodology; Supervision; Writing – review & editing.

Benita M Suckling: Methodology; Writing – review & editing.

Jonathan Penm: Methodology; Supervision; Writing – review & editing.

Acknowledgements

The authors acknowledge the assistance provided by Laura Hunter, nurse unit manager, for her contribution to the study.

Declaration of conflicting interests

The authors declare no conflicts of interest with respect to the research, authorship, and/pr publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: SL, JAS, JMN, AEP and JP are partly supported by an AVANT Research Foundation Grant. SL is supported by an NHMRC Postgraduate Scholarship and a Prince of Wales Hospital Foundation Grant. These funding sources had no role in the design of the study, data collection or analysis, or preparation of the manuscript.

ORCID iDs

Shania Liu

Jennifer A Stevens

Jed Duff

Joanna R Sutherland

Jonathan Penm

Data availability statement

The data that support the findings of this study are available from the corresponding author on reasonable request.

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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