Introduction

Opioid prescribing is an internationally recognised threat to population health and a pressing challenge for healthcare services [1–5]. Prescription opioid use in the US has fallen little from 2010 peaks, despite increased awareness of risks and opioid abuse [6]. North America is experiencing an ‘opioid crisis’, with rapidly rising opioid-related mortality, initially due to prescription opioids and more recently due to illicit heroin and fentanyl use, reaching a peak in 2016 [7]. Other higher-income countries risk following similar trajectories [8]. These trends are largely attributed to prescribing for chronic non-cancer pain [9], where opioids are no more effective than non-opioid pain medications and are associated with increased falls, fractures, dependence, overdose, and mortality [10,11]. Despite increased awareness of the potential harms in opioid prescribing, prescription rates remain historically high in both North America and Europe [12–14].

Whilst a growing body of work has investigated problematic opioid prescribing [1,12,15–17], less attention has been paid to evaluating proposed solutions. A Cochrane review found inadequate evidence for interventions targeting opioid use in individuals with chronic pain [18]. However, more recent studies indicate the value of provider- and system-level interventions [19–21], including a multifaceted approach comprising nurse care management, an electronic registry, data-driven academic detailing, and electronic decision tools [20].

Observed large variations in opioid prescribing, up to 10-fold in a UK study of primary care practices, suggests that physician habits and norms are a major driver, rather than patient need and evidence of benefit [16]. An ‘upstream’ population approach would therefore aim to change physician behaviour around both initiating and continuing opioid prescribing. The audit and feedback approach involves giving healthcare providers a summary of their clinical performance over a specified period [22]. It generally has modest effects on healthcare practice, which can translate into substantial population impacts [22].

We devised and applied an evidence- and theory-informed feedback intervention, the Campaign to Reduce Opioid Prescribing (CROP), to reduce opioid prescribing in primary care by prompting physicians to initiate opioids with caution and review patients currently prescribed opioids with no clear individual benefit. We evaluated the effect of the feedback intervention on prescribing of opioids and, anticipating the possibility of unintended consequences, prescribing of other analgesics and referrals to musculoskeletal services.

Methods

Data sources and outcomes

Our primary outcome was the number of adults prescribed any opioid per 1,000 adults per month. Secondary outcomes included the number of adults prescribed any opioid per 1,000 adults per month in the high-risk groups highlighted in the feedback [16]. Co-prescription with antidepressants was used as a proxy for mental health illness to reflect our previous work that identified that mental health diagnoses are often poorly recorded in EHRs in the UK [31]. We collected retrospective aggregated, anonymised practice-level data for intervention and control CCGs through the centralised reporting of 2 EHR systems (The Phoenix Partnership SystmOne and EMIS Health), at monthly intervals for 3 periods: pre-intervention (1 July 2013 to 31 March 2016; 47 months), intervention (1 April 2016 to 31 March 2017; 12 months), and post-intervention (1 April 2017 to 31 December 2017; 9 months). We extracted data on numbers of adults prescribed opioids in the previous 8 weeks, excluding those ever coded with cancer, palliative care, or drug dependence. We categorised opioid strength according to World Health Organization reported potency [16]. ‘Weaker’ opioids (with or without acetaminophen or ibuprofen) comprised codeine, dihydrocodeine, tramadol, pethidine, meptazinol, and tapentadol. ‘Strong’ opioids comprised diamorphine, morphine, oxycodone, fentanyl, hydromorphone, buprenorphine (excluding preparations used for substance misuse), pentazocine, dipipanone, and papaveretum. We collected data to assess any potential wider impacts on prescribing for pain, specifically the number of adults prescribed non-steroidal anti-inflammatory drugs (NSAIDs) and gabapentinoids, and referrals to musculoskeletal services (see S3 Text for sample search). We converted the numbers of adults in a prescription category into monthly rates based on monthly numbers of relevant adults per practice. The denominator for all outcomes was the number of adults per practice per month, except for the number of adults aged over 75 years prescribed opioids, where the number of adults aged over 75 years per practice per month was used. No patient-level data were extracted to calculate morphine equivalent doses.

We collected monthly data on total opioid prescriptions from the publicly available OpenPrescribing database for the same time periods, to assess overall opioid prescribing trends for all intervention and control practices [32]. We converted the monthly prescribing data into opioid prescribing monthly rate per 1,000 patients based on the 2017–2018 practice list size. We collected data from the 2017–2018 Public Health England National General Practice Profiles [33] for practice-level variables, comprising practice list size; female-to-male patient ratio; percentage of patients with long-term conditions, as a proxy for disease burden; and percentage of patients reporting a positive experience of their practice, as a marker of satisfaction with care. We used the percentage of patients in employment and practice-level Index of Multiple Deprivation (IMD) score as markers of deprivation. The IMD measures area deprivation and is determined for each patient on the list, where available, and then averaged over the practice. We used overall achievement in the clinical domain of the Quality and Outcomes Framework—a performance management system whereby primary care practices are remunerated according to achievement of targets—as a measure of overall quality of care [34].

We estimated intervention costs based on known costs (e.g., postage and data extraction fees) and time spent by staff (full-time equivalent salaries). Potential opioid prescription savings were calculated based on national opioid prescription costs and trends for the West Yorkshire population. A formal economic analysis was not conducted.

Results

Intervention practices were similar to control practices but generally had larger list sizes, fewer patients with long-term conditions, and more deprived populations (Table 1). Before the intervention, the mean rate of adults prescribed opioids per 1,000 adults per month was 58.1 in intervention practices and 62.2 in control practices (Table 2). The number of patients at higher risk of long-term or stronger opioid prescribing who were prescribed opioids; the number of patients prescribed NSAIDs, gabapentin, or pregabalin; and the number of patients referred to musculoskeletal services were similar between intervention and control.

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Table 1. Summary of practice characteristics.

https://doi.org/10.1371/journal.pmed.1003796.t001

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Table 2. Summary of opioid prescribing and other outcome-related characteristics at baseline for intervention and control practices.

https://doi.org/10.1371/journal.pmed.1003796.t002

For the primary outcome, the rate of any opioid prescribing rose across all practices during the pre-intervention period, increasing more in control than in intervention practices, with an adjusted change in rate of 0.36 (95% CI 0.27, 0.46) and 0.18 (95% CI 0.11, 0.25) adults prescribed opioids per 1,000 per month, respectively (Table 3). During the intervention period, the opioid prescribing rate rose by 0.53 per 1,000 per month (95% CI 0.29, 0.77) in control practices but fell in intervention practices by 0.12 per 1,000 per month (95% CI −0.30, −0.07), a difference of 0.65 (95% CI −0.95, −0.35; Fig 1). Post-intervention, the opioid prescribing rates decreased in both groups, with a smaller difference in mean change per month between the control and intervention practices of 0.26 (95% CI −0.57, 0.05). By the final month of follow-up, there was a mean difference of 7.4 (95% CI −17.4, 2.6) per 1,000 adults prescribed opioids between control and intervention practices. We estimate that this corresponds to around 15,000 fewer adults prescribed opioids during the intervention year in our total intervention population of 1.9 million. Estimated intervention effects changed little after adjustment for practice characteristics, and therefore adjusted estimates are shown.

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Fig 1. Mean number of adults prescribed opioid per 1,000 adults: multilevel linear model estimates: Electronic health record data and denominator.

Adjusted for percent female, Quality and Outcomes Framework score, percentage of patients reporting a positive experience of their practice, percentage of patients with long-term conditions, and Index of Multiple Deprivation. Black line = intervention practices; grey line = control practices.

https://doi.org/10.1371/journal.pmed.1003796.g001

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Table 3. Mean number of adults prescribed opioid per 1,000 adults and mean change per month: multilevel linear model—electronic health record data and denominator.

https://doi.org/10.1371/journal.pmed.1003796.t003

We observed trends generally favouring the intervention for groups at higher risk of long-term or stronger opioid prescribing. During the intervention, the rate of strong opioid prescribing decreased more in intervention than control practices (−0.11; 95% CI −0.13, −0.08), although rates in both groups similarly declined post-intervention. The rate of opioid prescribing in those aged 75 years and over decreased more in intervention practices than in control practices during the intervention period (−1.76; 95% CI −2.33, −1.19), with a sustained, if reduced, post-intervention difference (−0.63; 95% CI −1.20, −0.06). During the intervention period, rates of opioid prescribing fell more per month in intervention practices than in control practices in adults co-prescribed an antidepressant (−0.18; 95% CI −0.30, −0.06) and in adults with a mental health diagnosis (−0.24; 95% CI −0.35, −0.14), although post-intervention differences were not sustained. Rates of co-prescribed benzodiazepines did not differ significantly between intervention and control practices.

Regarding other analgesics, we observed declining pre-intervention trends for NSAID prescribing, with a larger decrease in intervention practices than control practices during the intervention (−0.35; 95% CI −0.47, −0.24) and both groups having similar post-intervention decreases. Rates of gabapentin prescribing decreased more in intervention practices than control practices during the intervention period (−0.25; 95% CI −0.42, −0.07), but this was not the case for pregabalin prescribing (0.01; 95% CI −0.16, 0.18). We observed no differences in rates of musculoskeletal referrals between intervention and control practices during the intervention period (0.02; 95% CI −0.008, 0.05) or after (0.01; 95% CI −0.04, 0.02).

Using publicly available data for total opioid prescriptions, we observed rising pre-intervention trends for both groups, a small decline during the intervention in intervention practices (−0.1; 95% CI −0.19, −0.01), and fairly static post-intervention rates in both groups (Table 4).

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Table 4. Mean number of prescriptions for opioids per 1,000 adults: Multilevel linear model—OpenPrescribing data, Public Health England National General Practice Profiles denominator.

https://doi.org/10.1371/journal.pmed.1003796.t004

The results of a simple (uncontrolled) ITS of intervention practices mirrored those of the controlled ITS (Table 5). This provides greater confidence that any association between the intervention and the effect is likely to be causal, and provides evidence that the control practices did not experience some other event [35].

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Table 5. Mean number of adults prescribed opioid per 1,000 adults: Multilevel linear model—electronic health record data and denominator, intervention only model (n = 313).

https://doi.org/10.1371/journal.pmed.1003796.t005

We estimated that the feedback intervention cost approximately US$66,000 to deliver, including US$52,000 in staff costs, US$3,200 in data extraction fees, and US$5,200 in stationary costs. Nationally, opioid prescription costs rose by approximately US$26,000 per 100,000 population during the intervention year. The reduction in opioid prescribing equated to around US$1,155,000 savings across intervention CCGs. The intervention gave overall cost savings of US$1,000,000 once all costs were accounted for.

Discussion

We observed that repeated evidence- and theory-informed comparative feedback reversed a rising trend of opioid prescribing in primary care, with sustained, if attenuated, effects. We have therefore demonstrated a successful, scalable strategy to reduce population-level opioid prescribing. The feedback intervention had a modest effect, with a difference of 0.65 fewer adults prescribed any opioid per 1,000 per month in intervention practices compared to control practices. However, at a population level, there were substantially fewer patients taking prescribed opioid medications.

The number of patients prescribed strong opioids fell during the intervention, although at a slower rate than the number of patients prescribed any opioid, possibly reflecting a longer de-prescribing process than for weaker opioids, given the need for gradual reductions to limit withdrawal symptoms. The intervention also had sustained effects for patients in targeted high-risk groups, including adults with coded mental health diagnoses and those co-prescribed antidepressants. The greatest effect was in adults aged 75 years and older, with a greater reduction in intervention practices than control practices of almost 1.8 adults aged 75 years and older prescribed opioids per 1,000 per month. This is important given the heightened risks of premature mortality, associated falls, and unplanned hospital admissions in this population [38,39].

Contrary to expectations, we observed reductions in wider analgesic prescribing not specifically targeted by feedback, specifically of NSAIDs and gabapentin, and no increases in referrals to musculoskeletal services. This provides some reassurance that the intervention had few rebound effects on wider service utilisation and costs. Indeed, it may have prompted primary care physicians to think differently about the value of prescribing analgesics in chronic non-cancer pain, and to prefer self-management options.

Prescribing data from publicly available sources [32] confirm that the intervention changed the underlying trend of rising opioid prescriptions, although it levelled off rather than fell. The smaller effect in this dataset is likely due to additional ‘noise’ in these data, which include prescriptions for cancer pain and drug dependency, especially as primary care physicians are encouraged to prescribe stronger opioids earlier and longer for palliative care [40].

There is a growing evidence base on the value of provider- and system-level interventions to reduce opioid use in adults with chronic non-cancer pain [19–21,41,42]. We provide evidence for a relatively efficient and scalable population strategy to address prescribing of both weaker and stronger opioids. The widespread use of EHR systems means that primary care prescribing data can be used to both drive and monitor change at a relatively low cost [43–45]. Our estimated costs suggest this intervention is relatively efficient given potential savings in projected opioid prescription costs.

Our intervention incorporated a range of evidence- and expert-informed suggestions to improve the effectiveness of feedback, such as providing repeated feedback with comparators to reinforce desired behaviour, recommending specific actions, and ensuring credibility of information [30]. However, the success of our strategy may also have depended upon contextual factors, specifically the timing and nature of the targeted clinical behaviour [46]. The intervention occurred during a period when primary care physicians were becoming increasingly aware of an opioid prescription problem and recognised a need for action. Feedback, used alone or with other interventions, may not be effective in changing all types of clinical behaviour [29]; opioid prescribing represents a relatively discrete behaviour that is reasonably within physician control [30].

We highlight 5 limitations. First, our study took place in a single region, potentially limiting generalisability to the rest of the UK and other healthcare systems. However, primary care physicians internationally report similar types of challenges in managing opioid prescribing [47], and performance feedback has been shown to work in many settings [22]. As only 1 out of 317 practices declined participation, selection bias is unlikely. We also demonstrated effects in a population with relatively high levels of socioeconomic deprivation, a factor that is associated with higher levels of opioid prescribing [48].

Second, routinely collected data are prone to coding errors. Such errors are less likely for prescribing data, but our use of ‘ever coded’ diagnoses may have overestimated current diagnoses, especially cancer and drug dependence. Some practices may have responded to feedback by re-categorising patients as drug dependent, thereby taking them out of the denominator and inflating intervention effects. However, we observed similar patterns of reductions in OpenPrescribing data. Sensitivity analysis showed that ‘extreme’ values, possibly due to coding errors, did not affect model estimates. Furthermore, the modelling approaches accounted for missing data within practices.

Third, the quasi-experimental design cannot fully account for concurrent interventions. Our previous publication showing the rise in prescribing in this area may have alerted practices to rising opioid prescribing [16]. Media attention to the North American ‘opioid crisis’ during the intervention period may also have influenced prescribing behaviour. Media coverage of the scale of UK opioid prescribing began towards the end of the intervention period and is unlikely to significantly account for observed changes in opioid prescribing [49,50]. Use of control practices [26] and a simple ITS analysis provides greater confidence that any association between intervention and effect is likely to be causal.

Fourth, this study did not specifically examine the acceptability of the feedback reports and whether or how they were used by general practices. This will be addressed in a separate process evaluation.

Fifth, we cannot be certain whether reductions in opioid prescribing were always clinically appropriate as we did not assess individual patient clinical indications and outcomes. The absence of any increases in prescribing of other potentially harmful analgesics and in referrals suggests that the intervention did not generate increased demand.

Patients have strong expectations for prescription pain relief, making reductions in prescribing challenging if they are perceived as undermining therapeutic relationships and patient satisfaction. Strategies to bring about significant improvements in healthcare delivery are unlikely to succeed if they fail to address multiple barriers and enablers. Addressing the rise of opioid prescribing and its legacy is likely to require sustained, coordinated efforts across all levels of healthcare systems that target organisational, clinical, and patient behaviours [51]. Performance feedback offers one approach that can be coupled with complementary educational campaigns and decision support to change physician prescribing habits and patient expectations [22]. We welcome further research to determine whether our findings can be replicated in other healthcare systems. There are further opportunities to evaluate and enhance feedback effectiveness, ideally involving head-to-head comparisons of different ways of delivering feedback within randomised designs [52].

Conclusions

We observed that an evidence- and theory-informed feedback intervention reversed rising opioid prescribing trends in a primary care setting. Effects decreased following cessation of the feedback, which may need to be sustained for maximum long-term impact. We observed no concurrent increases in prescribing of other analgesics or demand for musculoskeletal services. Feedback therefore offers a scalable approach to reduce population-level opioid prescribing.

Supporting information

Acknowledgments

We would like to thank Mohammed Imran at West Yorkshire Research and Development for his role in data collection.