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Has Province-Wide Symptom Screening Changed Opioid Prescribing Rates in Older Patients With Cancer?

Publication: Journal of Oncology Practice

Abstract

Purpose:

Previous work in Ontario demonstrated that 33% of patients with cancer with severe pain did not receive opioids at the time of their pain assessment. With efforts to increase symptom screening and management since then, the objective of this study was to examine temporal trends in opioid prescribing.

Methods:

The cohort was comprised of Ontario residents ≥ 65 years of age with a cancer history who were eligible for the government pharmacare program and had a pain assessment using the Edmonton Symptom Assessment System. Use of the Edmonton Symptom Assessment System is part of a provincial initiative to screen ambulatory patients with cancer for symptoms. Annually between 2007 and 2013, we used the date of an individual’s highest pain score as the index date to calculate annual opioid prescription rates for claims within 30 days before and up to 7 days after the index date. A logistic regression model evaluated the association between index year and odds of receiving an opioid prescription.

Results:

During the study period, the number of individuals undergoing symptom assessment annually increased more than eight-fold. Opioid prescription rates were directly related to pain scores, but there was an annual 5% relative decrease in the odds of receiving an opioid prescription during the era from 2009 to 2013.

Conclusion:

We are doing better at screening for pain, but this has not led to an increase in analgesic intervention for those identified. Additional work is required to determine what opioid prescribing rate is optimal to ensure we are not missing opportunities to improve patient comfort.

Introduction

The medical literature documents a long history of the undermanagement of cancer pain.1-3 This may be improving but is still a problem. A recent systematic review found that in studies published from 1994 to 2000, the mean proportion of patients with a negative pain management index (PMI), indicating inadequately treated pain, was 47% compared with 32% in studies published between 2006 and 2013.2 In Ontario, Canada, previous work from our group evaluated a population-based cohort of individuals with cancer ≥ 65 years of age with a pain score of 7 to 10 (on a scale of 0 to 10). We observed that 33% of patients did not receive a prescription for opioids in the 30 days before the date of their pain assessment or in the 1 week after.4
In Ontario, a province-wide initiative was started in 2007 to screen ambulatory patients with cancer for various symptoms during their visit to an ambulatory cancer clinic with the aim of improving interventions to manage patient symptoms. Ambulatory patients with cancer who attended a regional cancer center completed the Edmonton Symptom Assessment System (ESAS), a patient-reported outcome measure of general symptoms seen in many patients with cancer, including pain.5 The symptom screening program has been ongoing since its initiation, and its penetration and spread have increased considerably over time. Currently, approximately 30,000 patients are screened each month.
The purpose of this study was to evaluate the annual trends in the proportion of patients with cancer with pain who receive opioid medication. We hypothesized that the proportion of older patients in Ontario with severe pain who receive a prescription for opioids will have increased as a result of increased penetration of symptom screening as a standard of care, greater awareness of the symptoms experienced by patients with cancer, and a shift in practice with more attention to symptom management.

Methods

Study Design and Population

We performed a population-based time-trend analysis of opioid prescribing rates among patients with cancer in Ontario, Canada—from 2007 to 2013. For each year, our study population included ambulatory patients ≥ 65 years of age, all of whom were eligible for the government’s paid pharmacare program, known as the Ontario Drug Benefits Program. Cohort eligibility criteria also included a history of cancer before the study year and a pain assessment using the ESAS during the study year. ESAS is a valid and reliable tool used to assess severity of nine symptoms often experienced by patients with cancer scored on a scale of 0 to 10, with 10 being the worst.5,6 When the ESAS screening program was introduced in 2007, patients with lung cancer and those attending a palliative care clinic were specifically targeted. Over time, symptom screening has expanded to all patients with cancer, with each patient able to complete the ESAS at each ambulatory visit to a cancer center.7 Because a patient may have multiple assessments in a year, for each study year, the date of each patient’s highest ESAS pain score was chosen as the index date; this score was used to classify patients into mutually exclusive pain severity groups defined as follows: 0, no pain; 1 to 3, mild pain; 4 to 6, moderate pain; and 7 to 10, severe pain.8

Data Sources

We linked multiple population-based administrative health care databases from Ontario, Canada, using unique encoded identifiers. Patients with an ESAS pain assessment were identified from symptom data held by Cancer Care Ontario. Details about the ESAS assessment methodology have been described elsewhere.7,9 Briefly, on a visit to a cancer center, all patients with cancer complete a standardized ESAS assessment at a touch-screen kiosk or on paper before their visit with the health care team. It is then transferred or entered into the provincial symptom database. Symptom scores, as well as previous assessment scores, are printed for the patient to discuss with his or her clinical team to facilitate appropriate management.
Patients identified in the symptom database were linked to the Ontario Registered Persons Database, which contains basic demographic information about anyone who has ever had an Ontario health card number, to obtain sociodemographic and vital statistics information. The Ontario Cancer Registry, which includes diagnostic information on almost every Ontarian who has ever been diagnosed with cancer since 1964, was used to obtain cancer diagnosis information. The Ontario Drug Benefits Program database contains information on all prescription drug claims paid to pharmacists by the program, including opioids.

Outcomes

Our primary outcome was the proportion of patients with any opioid prescription claim within 30 days before and up to 7 days after the index date. This interval was used in a prior study that showed similar results in sensitivity analyses of different intervals, including up to 90 days of look back.4 Opioids of specific interest were identified from a previous study and included fentanyl, long-acting oxycodone (older and newer formulations), other long-acting opioids (eg, long-acting morphine, long-acting hydromorphone), immediate-release single agents (eg, morphine, hydromorphone), and immediate-release combination agents (eg, acetaminophen plus codeine).10 Only oral and transdermal formulations were included. Because we hypothesized that neuropathic agents could be prescribed as an alternative to opioids, in a secondary analyses, we also examined neuropathic agent prescription rates, specifically of gamma-aminobutyric acid derivatives (eg, baclofen, pregabalin, and gabapentin) and tricyclic antidepressants.

Statistical Analysis

Descriptions of our yearly study populations were examined using medians and interquartile ranges for age and counts and proportions for categorical characteristics. To examine the representativeness of the ESAS study population of the overall population with cancer, we also determined characteristics of the unscreened population, identified through the Ontario Cancer Registry.
A logistic regression model was implemented to examine the association between year of pain assessment (observation year) and having an opioid prescription (yes or no) within 30 days before and up to 7 days after the index date (date of pain assessment). A generalized estimating equation approach under an exchangeable correlation structure was used to account for correlation among repeated outcome measures from each individual. Variables included in the model were age at index date, sex, cancer type, neighborhood income quintile (based on postal code11), Charlson comorbidity index12 (0 if absent or missing v ≥ 1; cancer scores excluded), years between diagnosis date and index date, observation year, pain score (continuous), and cancer center. We included an indicator variable labeled era to denote 2007 to 2008, the time period when mostly patients with lung cancer and patients receiving palliative care were evaluated with ESAS, versus 2009 onward, when screening was expanded to all patients with cancer. We also included an interaction term between era and observation year to determine whether the annual rate of change varied by era.
This study was approved by the Sunnybrook Health Sciences Centre research ethics board. All analyses were conducted at the Institute for Clinical Evaluative Sciences using SAS, version 9.4 (SAS Institute, Cary, NC).

Results

Table 1 lists the characteristics of the study populations from 2007 to 2013. With increased uptake of the ESAS tool over time, the number of patients who were screened increased more than eight-fold from 2007 to 2013. The median age of patients was stable over the study period at 74 years. Women composed < 50% of the cohort. Between 2007 and 2013, increased use of the ESAS beyond patients with lung cancer and patients receiving palliative care contributed to a decrease in the proportion of patients with severe pain from 21.2% to 11.5%. In 2013, comparing patients who were and were not screened with ESAS, screened patients were younger and more likely to live in a higher income neighborhood. Differences in screening by cancer type were also found (data not shown).
Table 1. Baseline Characteristics of Cohort
Table 2 lists the observed annual prescription rates for opioids and neuropathic agents by pain severity and year. Opioids are prescribed more frequently for those with worse pain. However, in all instances, the opioid prescription rate decreased over time. Small increases in prescriptions for neuropathic agents were observed.
Table 2. Observed Proportion of Patients With a Prescription Within 30 Days Before and Up to 7 Days After Index Date by Pain Severity and Year
Table 3 lists univariable and multivariable generalized estimating equation model results. In the adjusted model, higher odds of receiving an opioid prescription were associated with increasing pain, decreasing age, lung cancer, living in a poorer neighborhood, and having comorbid illness. There was an annual 5% relative decrease in the odds of receiving an opioid prescription during the era of 2009 to 2013 (odds ratio, 0.946; 95% CI, 0.938 to 0.954).
Table 3. Generalized Estimating Equation Model Result for Outcome of Opioid Prescription Within 30 Days Before and Up to 7 Days After Pain Assessment for Patients Age ≥ 65 Years
Figure 1 shows the observed odds of receiving an opioid prescription by year. In 2007, the odds of filling a prescription for opioids was 0.464 (95% CI, 0.441 to 0.488), which decreased to 0.158 (95% CI, 0.154 to 0.162) in 2013. This observed decrease over time is consistent with results obtained from the multivariable regression model.
Fig 1. Observed odds of receiving an opioid prescription within 30 days before and up to 7 days after pain assessment by year.

Discussion

Our results fail to support our hypothesis of increased opioid prescribing over time. Increased penetrance of a symptom screening program has not led to increased opioid prescribing, and in fact, prescribing is decreasing. The inference is that, given the literature on undermanagement of pain, a quality gap in the management of pain in patients with cancer remains. However, it is worth noting that almost half of patients did not report any pain.
Our findings may have several explanations. First, current efforts around measurement and quality improvement in this area have focused on improving patient screening rates with less attention to symptom management, specifically the use of screening data to inform patient management or use of best practices.2,13,14 If systems were accountable for symptom management rather than symptom screening, perhaps more change would have been seen. Numerous reviews have shown that screening alone rarely translates into better health outcomes and numerous barriers must be overcome to embed use of symptom screening data in routine practice.15,16 During the early era of symptom screening, there was extensive focus on changing clinicians’ practices in symptom management and on quality improvement in symptom management using a collaborative approach,17 with less attention paid to long-term sustainability. In addition, the first 2 years of the ESAS screening program targeted patients with lung cancer and patients receiving palliative care. Practitioners caring for these patients may have had greater pain management experience and a different perspective on pain management, compared with practitioners caring for the broader population with cancer screened thereafter. This might include a greater likelihood of referring patients to pain specialists. Our analysis did account for as many possible confounders as possible, including this possible effect of era.
An alternative explanation is that despite a province-wide program to screen for symptoms and the publication of pain management symptom guides, the increasing scrutiny of opioid prescribing in the general population and an increasing opioid phobia have negatively impacted cancer pain management.10,18 It has been clearly documented that a lack of knowledge and concerns about addiction are known factors affecting opioid prescribing among health care providers and patients.19,20
This study’s findings support previous studies in Ontario and elsewhere. In a systematic review of studies published between 2007 and 2013 from multiple countries including Canada, the rate of undertreatment of pain among patients with cancer measured using the PMI was reported as 31.8%.2 The PMI considers the severity of pain and strength of analgesic to quantify the adequacy of pain control, with negative scores indicating inadequacy.21 Although much smaller and limited to palliative patients, other Ontario studies have reported that > 25% of patients have a negative PMI, with the proportion also increasing over time.14,22 It is clear that pain management in patients with cancer has not improved over time despite this provincial symptom screening program.
Strengths of this work include the large study population and comprehensive nature of the data used, including all regional cancer centers across the province. However, limitations include not knowing the causes of pain. Because scores were collected from ambulatory patients attending Ontario’s cancer centers, we also did not capture pain among patients too sick to come for a visit, specifically patients at the end of life at home, in a hospital, or in a palliative care unit.
There is increasing interest in using standardized symptom assessments in routine clinical care. Implementation of such measures requires concerted effort not only for symptom assessment but also for the response and sustainability of the changes in practice. For pain, other issues may be relevant, including the increased scrutiny around prescribing, which may increase barriers to good pain control for patients with legitimate indications. Additional work is required to determine what the optimal opioid prescription rate is and to develop a robust measure of the adequacy of pain management to help drive quality improvement in this area.
Table 4 Observed Proportion of Patients With a Prescription Within 30 Days Before and Up to 7 Days After Index Date by Pain Severity and Year

Acknowledgment

Supported by the Ontario Institute for Cancer Research through funding provided by the Government of Ontario and by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). Parts of this material are based on data and information provided by Cancer Care Ontario (CCO). The opinions, results, views, and conclusions reported in this article are those of the authors and do not necessarily reflect those of CCO and are independent from the funding sources. No endorsement by ICES, MOHLTC, or CCO is intended or should be inferred.

Authors' Disclosures of Potential Conflicts of Interest

Has Province-Wide Symptom Screening Changed Opioid Prescribing Rates in Older Patients With Cancer?

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jop/site/ifc/journal-policies.html.

Lisa Barbera

Other Relationship: Cancer Care Ontario (provincial cancer agency, not-for-profit)

Rinku Sutradhar

No relationship to disclose

Anna Chu

No relationship to disclose

Hsien Seow

No relationship to disclose

Craig C. Earle

Patents, Royalties, Other Intellectual Property: UpToDate

Mary Ann O'Brien

No relationship to disclose

Deborah Dudgeon

No relationship to disclose

Carlo DeAngelis

Consulting or Advisory Role: Merck, Astellas Pharma
Speakers' Bureau: Merck, Roche
Research Funding: Boehringer Ingelheim, Novartis, Boehringer Ingelheim

Clare Atzema

No relationship to disclose

Amna Husain

Employment: My Health Partners (I)
Leadership: My Health Partners (I)
Stock or Other Ownership: Johnson & Johnson, Proctor & Gamble, My Health Partners, Induce Biologics
Honoraria: AstraZeneca (I), Boehringer Ingelheim (I), Janssen (I), Merck (I), Novo Nordisk (I)
Consulting or Advisory Role: AstraZeneca (I), Boehringer Ingelheim (I), Janssen (I), Merck (I), Novo Nordisk (I)
Research Funding: AstraZeneca (I), Merck (I), Novo Nordisk (I)
Patents, Royalties, Other Intellectual Property: Intellectual property in Clinical Collaboration Software called Loop, three provisional patents on actions of GLP-1 and related peptides (I)
Travel, Accommodations, Expenses: AstraZeneca (I), Boehringer Ingelheim (I), Janssen (I), Merck (I), Novo Nordisk (I)

Ying Liu

No relationship to disclose

Doris Howell

No relationship to disclose

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Information & Authors

Information

Published In

Journal of Oncology Practice
Pages: e927 - e934
PubMed: 28926291

History

Published online: September 19, 2017

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Authors

Affiliations

Lisa Barbera [email protected]
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Rinku Sutradhar
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Anna Chu
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Hsien Seow
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Craig C. Earle
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Mary Ann O’Brien
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Deborah Dudgeon
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Carlo DeAngelis
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Clare Atzema
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Amna Husain
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Ying Liu
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada
Doris Howell
Odette Cancer Centre, Sunnybrook Health Sciences Centre; Institute for Clinical Evaluative Sciences; University of Toronto; Sunnybrook Health Sciences Centre; Mount Sinai Hospital, Temmy Latner Centre for Palliative Care; University Health Network, Princess Margaret Hospital, Toronto; McMaster University, Hamilton; and Queen’s University, Kingston, Ontario, Canada

Notes

Corresponding author: Lisa Barbera, Odette Cancer Centre, 2075 Bayview Avenue, Toronto, Ontario, Canada, M4N 3M5; e-mail: [email protected].

Author Contributions

Conception and design: Lisa Barbera, Rinku Sutradhar, Anna Chu
Financial support: Lisa Barbera
Data analysis and interpretation: All authors
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors

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Lisa Barbera, Rinku Sutradhar, Anna Chu, Hsien Seow, Craig C. Earle, Mary Ann O’Brien, Deborah Dudgeon, Carlo DeAngelis, Clare Atzema, Amna Husain, Ying Liu, Doris Howell
Journal of Oncology Practice 2017 13:11, e927-e934

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