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Owen Sound event shines light on urgent need to tackle opioid epidemic

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Gelja Sheardown was brought to tears by the number of local lives lost due to opioid overdoses since the start of the COVID-19 pandemic just over two years ago.

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“Forty-eight. I didn’t realize it has been that many. That is a lot,” Sheardown said after hearing the number of Grey-Bruce residents whose deaths had been confirmed as being a result of an opioid overdose.

In 2020, the first year of COVID-19 pandemic, fatal opioid overdoses spiked to 24 in the two counties, up from 16 the year before. According to the Grey Bruce Health Unit, through the first half of 2021 there had been 15 confirmed fatal opioid overdoses.

Alison Govier, who is the co-ordinator of the Community Drug and Alcohol Strategy, said Saturday during the We Will Remember Them opioid overdose awareness event in downtown Owen Sound on Saturday that more urgency needs to be put into addressing the crisis.

“What we have done so far it hasn’t worked,” said Govier. “Since the beginning of COVID we have lost – and this is an underestimation – at least 48 community members.

“These are our husbands, our neighbours and our friends.”

Govier said they are calling on all levels of government to prioritize the opioid epidemic with a determination similar to that that has been shown during the virus pandemic.

“We have learned so much from COVID how if all levels of government and across all sectors and agencies and community we work together then we can solve big complex problems,” Govier said.

On Saturday, Govier thanked Sheardown for the work she has done to raise awareness and in doing so, helping to save lives.

“I have so much appreciation for Gelja in bringing this into the forefront of awareness with events such as this,” Govier said.

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It was two years ago on Sunday that the opioid epidemic impacted Sheardown in a very personal and tragic way when she lost her husband Barrett Warwick to a fentanyl overdose.

On Saturday she was at the Owen Sound Farmers’ Market for the awareness event, meant to shine a light on the ongoing epidemic, highlight the local supports available, and give people a chance to reflect, remember and honour those loved ones they have lost.

Participants were invited to take a carnation, walk the block that includes the 8th and 9th street bridges and then drop the flower into the river, to symbolize the loss of those who have died and are gone permanently from the community. Representatives from the Grey Bruce Health Unit, Safe ‘n Sound, United Way of Bruce-Grey and Community Drug and Alcohol Strategy were on hand providing information and resources. Life-saving Naloxone kits were being made available.

The event was started last year at the urging of Sheardown, who wanted to shine a light on the crisis and help to prevent others from going through what she, her three young children, and the rest of her family and friends have had to go through.

“It is about bringing awareness that this is a big problem in Grey-Bruce and addressing all the things that people can do to try to help if they know somebody who has problems or is needing help,” Sheardown said. “It is very important for me to raise awareness after losing my husband.”

Warwick died in the family’s home in the early morning hours of March 27, 2020 after taking fentanyl, a powerful opioid that is 50 to 100 times more  powerful than morphine. He suffered from depression and had a back injury and had been using painkillers like Percocet to ease the pain of both. Sheardown was unaware of many of the struggles her husband was going through and his death was a shock to her.

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While Warwick’s death has been very difficult for Sheardown and her family and friends, she said they all have made it a priority that the opioid epidemic be a major focus in the community.

“We don’t want it to be silenced anymore and we want to end the stigma around drug addicts,” Sheardown said. “These are hard working individuals, these people have families. They are good people who just get caught in a bad place and it is usually because of past trauma, mental health issues and stuff like that.”

Sheardown, who has suffered with addiction in the past herself and managed to beat it, wants to be able to lend her voice to be able to help as many people as possible.

“I just want everyone to know that the clean, sober life is so much better and we can all get there,” Sheardown said.

Govier’s position as co-ordinator of the Community Drug and Alcohol Strategy is resourced by Grey and Bruce counties and embedded in the Addictions Services team of the Canadian Mental Health Association of Grey Bruce. The strategy includes a network of community partners who work towards improving the quality of life for local individuals, families and communities by reducing the health and social harms associated with substances.

The strategy has developed a series of calls to action to address the crisis.

Locally that includes working with community partners to expand and enhance harm reduction outreach as well as assessing and incorporating harm reduction into current policies and practices.

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At the provincial level, they would like to see a task force formed that would have monitoring and surveillance through engaging people with lived experience to understand the full magnitude of the problem to create solutions.

They are also calling on the provincial and federal governments and other regulatory agencies to take the necessary steps to implement and support safe supply initiatives.

Also at the federal level, the strategy is asking the drug poisoning crisis to be declared a national emergency so the crisis is met with the urgency it deserves, and that an action plan be developed to address the factors that lead to substance use and abuse and obstruct recovery.

Govier said the decriminalization of drugs for personal use and further development of a safe supply program is also important. She hopes initiatives at the local level  to decriminalize simple drug possession and focus on the trafficking of drugs trends across the country.

“The problem is that drug use is criminalized, so there is a danger and a risk attached to disclosing your drug use, so it is really hard to get an understanding of how many folks are at risk over overdose, because according to our federal prohibition laws they are committing a crime” Govier said. “That drives everyone into the shadows.

“It is a stigma in society and it is a product of our policies.”

Govier said that even when it comes to reporting deaths from opioid overdoses, there is at least a six-month lag as they wait for coroner data to be released.

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“It is really hard to respond appropriately and quickly enough if you don’t have an understanding of the problem,” said Govier. “What we can do is listen to people who are most directly affected and we can urge our government to really take this seriously.”

Joan Farley was at the walk on Saturday in memory of her son Grant who died in 2018 of an opioid overdose.

She also wants to bring more awareness to the opioid epidemic and the impact it is having on people.

“We need to do more to educate people and to help save them,” Farley said. “I think we need to raise awareness around stigma, that from my understanding, a lot of people don’t go for help because of stigma. That needs to be addressed.”

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Trajectories of prescription opioid dose and risk of opioid-related adverse events among older Medicare beneficiaries in the United States: A nested case–control study

Trajectories of prescription opioid dose and risk of opioid-related adverse events among older Medicare beneficiaries in the United States: A nested case–control study


Methods and findings

We conducted a nested case–control study within a cohort of older (≥65 years) patients diagnosed with CNCP who were new users of prescription opioids, assembled using a 5% national random sample of Medicare beneficiaries from 2011 to 2018. From the cohort with a mean follow-up of 2.3 years, we identified 3,103 incident ORAE cases with ≥1 opioid prescription in 6 months preceding the event, and 3,103 controls matched on sex, age, and time since opioid initiation. Key exposure was trajectories of prescribed opioid morphine milligram equivalent (MME) daily dosage over 6 months before the incident ORAE or matched controls. Among the cases and controls, 2,192 (70.6%) were women, and the mean (SD) age was 77.1 (7.1) years. Four prescribed opioid trajectories before the incident ORAE diagnosis or matched date emerged: gradual dose discontinuation (from ≤3 to 0 daily MME, 1,456 [23.5%]), gradual dose increase (from 0 to >3 daily MME, 1,878 [30.3%]), consistent low dose (between 3 and 5 daily MME, 1,510 [24.3%]), and consistent moderate dose (>20 daily MME, 1,362 [22.0%]). Few older patients (<5%) were prescribed a mean daily dose of ≥90 daily MME during 6 months before diagnosis or matched date. Patients with gradual dose discontinuation versus those with a consistent low dose, moderate dose, and increase dose were more likely to be younger (65 to 74 years), Midwest US residents, and receiving no low-income subsidy. Compared to patients with gradual dose discontinuation, those with gradual dose increase (adjusted odds ratio [aOR] = 3.4; 95% confidence interval (CI) 2.8 to 4.0; P < 0.001), consistent low dose (aOR = 3.8; 95% CI 3.2 to 4.6; P < 0.001), and consistent moderate dose (aOR = 8.5; 95% CI 6.8 to 10.7; P < 0.001) had a higher risk of ORAE, after adjustment for covariates. Our main findings remained robust in the sensitivity analysis using a cohort study with inverse probability of treatment weighting analyses. Major limitations include the limited generalizability of the study findings and lack of information on illicit opioid use, which prevents understanding the clinical dose threshold level that increases the risk of ORAE in older adults.


The number of older adults who had medical encounters for treatment of opioid misuse, dependence, and poisoning has increased disproportionately over the past decade [1]. The opioid-related adverse events (ORAEs) defined by the United States (US) government agencies [13] contain diagnostic codes commonly used for opioid use disorder (OUD) and overdose from use of illicit opioids (i.e., heroin) or incorrect use of prescribed opioids, as well as E codes for severe adverse effects from use of heroin or correct use of prescribed opioids that lead to hospital or emergency department visits. The rate of hospital stays and emergency department visits due to ORAEs rose by 34% (from 199.3 stays to 267.6 stays per 100,000 persons) and 74% (from 44.7 visits to 77.9 visits per 100,000 persons), respectively, among older patients between 2010 and 2015 [1]. A study of a commercially insured population also indicated a marked increase (14.2-fold from 2.05 to 31.12 per 10,000 persons) in the incidence of OUD or overdose among older adults aged 65 and older between 2006 and 2016 [4]. These alarming statistics have prompted questions of what might have predisposed older patients to be at risk for ORAEs [2].

Of the known risk factors, prescription opioid dose is one of the strong predictors of ORAEs [59]. Studies of nonelderly or mixed populations of young and older populations showed that use of prescription opioids at a dose of 90 morphine milligram equivalent (MME) or above per day was associated with an increased risk for opioid overdose and deaths [1013]. Built on this evidence, the 2016 Centers for Disease Control and Prevention (CDC)’s Guidance for Opioid Prescribing for Chronic Pain recommends avoidance of prescribing daily opioid doses at 90 mg MME or greater [14]. Since then, some medical societies’ guidelines [15], state regulations [16], and health insurance payers [17] have adapted the CDC-recommended dose threshold and limited prescribing doses of opioids to 90 mg MME per day [18,19].

The Centers for Medicare and Medicaid Services (CMS), the largest insurer of older adults in the US, has utilized 90 daily MME as one of the criteria for flagging high-risk beneficiaries for OUD or overdose and required its Part D plan sponsors to adjudicate the appropriateness of opioid prescribing of these high-risk patients [20]. However, our prior study has shown that the CMS’s opioid overutilization criteria missed the majority of patients with OUD or overdose and flagged more than half of opioid prescription users as high risk who were not diagnosed with OUD or overdose [21]. The finding challenges the use of 90 daily MME as a risky threshold for Medicare beneficiaries, the vast majority of whom are aged 65 years or older. Literature has primarily focused on establishing the high-risk prescription opioid dose thresholds using healthcare data among young (aged 18 to 64) individuals, which may not be applicable to older individuals who may have different thresholds for adverse opioid outcomes due to declined renal and hepatic function, multiple comorbidities, and polypharmacy.

Because of the time-varying nature of prescription opioid use, assessing the progression of opioid dose toward ORAEs is important to understand whether there are typical and atypical opioid dose patterns emerged before the adverse events. Thus, the present study aims to (1) examine trajectories of prescription opioid dose preceding the incident medical encounter for ORAEs; and (2) quantify the association between identified trajectories of prescribed opioid dose and risk of ORAEs among Medicare older adults with chronic noncancer pain (CNCP).


Study setting and cohort

Using the pharmacy and medical claims data from a 5% national random sample of Medicare beneficiaries from the US CMS [22], we conducted a nested case–control study design within a cohort of older (≥65 years) beneficiaries enrolled in US Medicare who were new opioid users and had a diagnosis of CNCP between January 1, 2011 and December 31, 2018. We chose a nested case–control study design because such design allows for examination of prescription opioid use in a time window (i.e., 6 months in this study) preceding ORAE outcome as a risk factor [23]. Studying prescription opioid use before ORAE is important because of the time-varying nature of opioid use, with a larger effect expected from opioid exposure preceding an ORAE compared to distant opioid exposure during the early months of opioid initiation.

Cohort members were older adults aged 65 or older and naïve to opioids for 12 months prior to the date of their first dispensed opioid prescription (i.e., cohort entry). During the 12-month pre-cohort entry, patients were also required to have the following: (1) continuous enrollment in Medicare Parts A (inpatient), B (outpatient provider), and D (prescription drug) without insurance coverage from Health Maintenance Organization or employer-sponsored plans; and (2) primary or secondary diagnosis of a chronic pain condition (S1 Table) to ensure a relatively homogeneous cohort regarding pain conditions. We excluded patients who received a cancer diagnosis, hospice care, or palliative care, as well as those with a history of an ORAE encounter during the year before cohort entry. Patients were followed until an ORAE event, a cancer diagnosis, receiving palliative or hospice care, death, Medicare disenrollment, or study end (i.e., December 31, 2018), whichever came first. The University of Florida Institutional Review Board approved the study with a waiver of informed consent and HIPAA authorization because of minimal risk and lack of feasibility to contact Medicare patients. Data analyses were performed as per a prespecified protocol between January and December 2020 (S1 Text). This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines (S1 STROBE Checklist).

Selection of cases and controls

In the cohort of opioid initiators, we identified cases of ORAE using the International Classification of Diseases, Ninth or Tenth Revision, Clinical Modification (ICD-9 or ICD-10 CM) Codes recorded in inpatient or outpatient encounter claims during follow-up. These codes have been used by the CDC and Agency for Healthcare Research and Quality (AHRQ) to define ORAEs, including opioid misuse (ICD-9 codes: 305.50–305.52), opioid dependence and unspecified use (304.00–304.02, 304.70–304.72), opioid poisoning (965.00–965.02, 965.09, 970.1, E850.0-E850.2), and adverse effects of opioids (E-codes: E935.0-E935.2, E940.1) [1,3]. We also used the ICD-9-CM to ICD-10-CM code conversion of ORAEs provided by AHRQ (S1 Table) [24]. Opioid misuse and dependence are commonly grouped as OUD, and opioid poisoning is also known as OD. The adverse effects of opioids defined by the E codes include any severe reactions to illicit opioids (i.e., heroin) or correct use of prescribed opioids (i.e., methadone, opioid antagonists, and other opioids) that lead to an emergency department or hospital visit. Consistent with prior studies [4,25], when identifying patients with incident ORAE encounter, we excluded ICD-10-CM codes that indicated “in remission” or “subsequent encounter.” The date of the first ORAE encounter represented the “index” date.

To emulate clinical practices where a limited time window of patient history is often available for routine clinical assessment, we focused on trajectories of opioid dose during the 6 months before the incident ORAE encounter for cases. To measure opioid dose trajectories, cases were required to have at least one prescription opioid fill in the 6 months before the incident ORAE. This requirement excluded cases who had an incident ORAE within 6 months after opioid initiation and who had no prescription opioid dispensed in the 6 months before an incident ORAE. The rationale for requiring one or more opioid prescriptions is to (1) focus on cases who visit clinics to obtain prescription opioids and have a chance of being evaluated by doctors for risk of ORAE before its onset; and (2) to reduce confounding by illicit opioid use, which likely occurs among cases who had no prescription opioid use before ORAE [4]. For each case, we used an incidence density sampling approach to randomly select one control person prescribed opioids who was at risk but had not experienced an ORAE encounter by the index date of the case event. In other words, controls also had at least one dispensed opioid prescription in the 6 months before their matched date. We matched controls to cases on age, sex, and time (in days) since cohort entry because these 3 matching variables provided a sufficient number of controls for matching. We adjusted for other nonmatched confounders later in multivariable regression models.

Prescription opioid and its dose conversion

Prescription opioids approved for use in the US between 2011 and 2018 were captured from the Medicare Part D Prescription Event files based on the National Drug Code (S2 Table). We excluded (1) injectable opioids because they are primarily used in inpatient settings where prescription dispensing data are not available; and (2) buprenorphine sublingual tablets and buprenorphine–naloxone combinations because they are indicated for treatment of OUD or OD.

The dose of each prescription opioid filled during 6 months before the index date was converted to an MME dose based on a standard formula—the quantity of opioids dispensed per day multiplied by the strength and the MME conversion factor [26]. We then calculated the mean daily MME dose in each month by adding the MMEs of all days with prescribed opioids dispensed during the month and then dividing by 30 days. Sensitivity analysis was conducted with the mean daily MME dose calculated at biweekly intervals.

Statistical analysis

We used a group-based trajectory model (GBTM) to identify clusters of patients who followed a similar longitudinal pattern for prescribed opioid dose during the 6 months preceding an incident ORAE encounter for cases and matched controls. Because the monthly mean MME measure had a nonnormal distribution, to enable model convergence while retaining all MME data points, we applied natural log transformation to the MME measure and modeled log-MME as a censored normal distribution [27]. We fitted the GBTMs with 1 to 5 classes and found that a model with 4 trajectories was optimal within the recommended criteria (S3 Table) [27,28]. Characteristics, as well as the use of cautionary high-dose (defined as 50 daily MME) and risky high-dose (defined as 90 daily MME) in any given month, were described and compared across the 4 trajectory groups using the chi-squared test. Sensitivity analysis was performed by examining prescription opioid dose trajectories among cases with specific types of opioid encounters and their matched controls.

We used a multivariate conditional logistic regression to examine the association between the identified 4 trajectories of prescribed opioid dose and risk for ORAE in the study sample of cases and controls, adjusting for several potential confounders measured between 12 and 6 months before the index date. These confounders included demographics (race/ethnicity [defined based on Research Triangle Institute race code available in the Medicare claims database and grouped into 3 groups: White, Black, and other (including Hispanic, Asian, Pacific Islander, and Native American individuals), each with a sample size sufficient enough to ensure statistically reliable estimates], low-income subsidy status [yes/no], region [Northeast, Midwest, South, and West]), diagnosis of alcohol or tobacco use disorder, types of chronic pain conditions (musculoskeletal, neuropathic, and idiopathic pain), polypharmacy (defined as having ≥5 different medications, excluding opioids), select clinical comorbid conditions (including mental health disorders, diabetes, cardiovascular diseases, hypertension, pulmonary condition, kidney disease, gastrointestinal disorder, respiratory infections, injuries, and infections from nonsterile opioid injection, identified based on ICD codes defined in the Clinical Classifications Software (CCS) of the Healthcare Cost and Utilization Project) [29], and overall healthcare utilization (including any hospital stay, any emergency department visit, and any skilled nursing facility stay, identified based on medical claims). To account for opioid exposure time, we also calculated the duration of opioid use between opioid initiation and the day before the 6-month exposure measurement period for each individual. To account for the secular trend in national opioid prescribing, we also included the year of index date (2011 to 2018) as a linear variable in the model. S1 Table details diagnostic or procedure codes of the aforementioned confounders. We reported the odds ratios (ORs) and 95% confidence intervals (CIs) from the model.

We performed additional analysis by assessing trajectories of prescribed opioid doses in relation to specific types of opioid encounters (i.e., opioid misuse or dependence and opioid poisoning). We conducted a sensitivity analysis using a cohort study design with inverse probability of treatment weighting (IPTW) analysis to test the association of trajectories of prescribed opioid dose with risk of ORAEs (see details of method in S2 Text). S4 Fig showed 4 trajectories of prescribed opioid dose identified in the sensitivity analysis of a cohort design, which resemble the shapes of the 4 groups identified in the main analysis using a nested case–control design. S5 and S6 Tables showed the baseline characteristics of the 4 identified trajectory groups before and after IPTW weighting, respectively, in a cohort design. After the weighting, all characteristics were balanced between the target and reference trajectory group, except for the duration of opioid use since opioid initiation, for which statistical adjustment was performed in the final weighted Cox hazard models as a sensitivity analysis. All analyses were performed using SAS 9.4, and all tests were two-sided with statistical significance set as P < 0.05.


A cohort of 380,272 Medicare older patients with CNCP (mean [SD] age, 76.2 [7.9] years; 65.4% female; and 81.4% White) were new users of prescription opioids between 2011 and 2018 (Table 1). Fig 1 describes the cohort inclusion and exclusion criteria. During the year before cohort entry, 9.0% had a diagnosis of tobacco or alcohol use disorder, 28.3% had mental health disorders, 44.7% had diabetes, and 59.3% had cardiovascular diseases. The majority (80.9%) had polypharmacy. Musculoskeletal pain was the most prevalent pain condition experienced in these older adults.


Table 1. Characteristics of the total cohort, cases who had an incident ORAE encounter, and matched controls of older adults with CNCP who were new users of prescription opioids between 2011 and 2018.

From this opioid new user cohort, we identified 6,176 patients who had an incident ORAE encounter during follow-up, yielding an incidence rate of 7.17 per 1,000 person-years. Of the 6,176 ORAE cases, 1,800 (29.1%) had the encounter within the first 6 months after prescription opioid initiation, and 1,273 (20.6%) had no prescription opioid fill in the 6 months preceding the ORAE diagnosis. This resulted in 3,103 cases with a 6-month follow-up preceding the ORAE, during which at least one opioid prescription was dispensed for dose trajectory analysis. Of 3,103 cases, 55.5% had a diagnosis of opioid misuse or dependence, 45.1% had a diagnosis of opioid poisoning, and only 0.06% had a diagnosis of adverse effects of opioids. Table 1 gives the characteristics of the 3,103 cases and 3,103 matched control patients.

We identified distinct opioid dose trajectories before an incident ORAE encounter or matched date in controls (Fig 2). Four trajectories, categorized based on their mean daily MME use of prescription opioids per month, included patients with gradual dose discontinuation (from ≤3 to 0 daily MME, consisting of 23.5% of the study sample), gradual dose increase (from 0 to >3 daily MME, 30.3%), consistent low-dose use (between 3 and 5 daily MME, 24.3%), and consistent moderate-dose use (>20 daily MME, 22.0%). The dose trajectory groups differed significantly for most demographics as well as select pain and clinical conditions (Table 2).


Fig 2. Trajectories of mean daily MME dose prescribed in each month for the 6 months preceding an incident encounter of ORAE for cases or matched controls of older adults.

Lines represent types of dose trajectory group, and for each line, each point represents the mean daily MME of prescription opioids per month. The scale on the left and right side of the figure is the natural logarithm of MME and actual MME, respectively. The error bar represents the standard deviation of the natural logarithm transformed MME. MME, morphine milligram equivalent; ORAE, opioid-related adverse event.

Compared with controls, cases had a lower proportion of patients with gradual dose discontinuation (11.8% versus 35.2%, P < 0.001) but a higher proportion with consistent moderate dose (31.6% versus 12.3%, P < 0.001) (Table 3). Overall, only 2.4% of older patients were prescribed a mean daily dose of 90 mg MME or more during any month of the 6 months before the index date. Sensitivity analysis with mean daily MME use of prescription opioids calculated at the biweekly interval showed similar dose trajectories (S1 Fig). We found similar prescription opioid dose trajectories among cases with opioid misuse or dependence encounters and their controls (S2 Fig) and among cases with opioid poisoning encounters and their controls (S3 Fig).

In adjusted multivariable conditional logistic regression analysis, prescription opioid dose trajectories were independently associated with risk for an ORAE encounter (Table 4). Compared to patients with gradual dose discontinuation, those with graduate dose increase had a 3.4-fold (95% CI 2.8 to 4.0; P < 0.001), those with consistent low dose had a 3.8-fold (95% CI 3.2 to 4.6; P < 0.001), and those with consistent moderate dose had an 8.5-fold (95% CI 6.8 to 10.7; P < 0.001) increased risk for having an incident ORAE encounter, after adjustment for covariates. Stratification analysis by specific types of opioid encounters showed similar results, with the gradual dose increase, consistent low dose, and consistent moderate dose group having an increased risk for opioid misuse/dependence or poisoning when compared to the group with decreasing dose trajectory (S4 Table).

Our main findings remained robust in the sensitivity analysis using a cohort study design. The increased risk of ORAE among patients with the gradual dose increase (adjusted hazard ratio [HR] = 4.4; 95% CI 3.8 to 5.1; P < 0.001), consistent low dose (aHR = 1.9; 95% CI 1.6 to 2.1; P < 0.001), and consistent moderate dose (aHR = 5.7; 95% CI 5.0 to 6.5; P < 0.001), as compared to those with the gradual dose discontinuation, persisted in a cohort design (S7 Table). Further stratification by the duration of follow-up revealed a higher risk of ORAE in the earlier months (i.e., first or second month) of the follow-up defined in the cohort design as a sensitivity analysis (S7 Table).


In this sample of older Medicare beneficiaries, we found that 4 trajectories of opioid dose prescribed during 6 months before the incident ORAE diagnosis or matched date emerged: gradual dose discontinuation (from ≤3 to 0 daily MME), gradual dose increase (from 0 to >3 daily MME), consistent low dose (between 3 and 5 daily MME), and consistent moderate dose (>20 daily MME). Overall, few older patients (<5%) were prescribed a mean daily dose of ≥90 daily MME before diagnosis or matched date. Compared to older patients with gradual dose discontinuation, those with gradual dose increase, consistent low dose, and consistent moderate dose use group had a higher risk of ORAE. The findings were consistent in a sensitivity analysis using a cohort design.

Across the 4 identified groups, we observed a low dose range (mean daily dose between 0 to 20 daily MME) of prescribed opioids and less than 5% of older patients, the majority of whom were among the cases, receiving doses at or above the dose threshold of 90 daily MME. Compared to evidence observed in younger adults with ORAE [25], a lower dose range (0 to 20 daily MME versus 3 to 150 daily MME) and a lower proportion with ≥90 daily MME (2.4% versus 28.8%) was observed among older adults with a similar diagnosis of ORAE, suggesting that there is a unique opioid dosage pattern preceding the incident ORAE encounter in the older population.

The mechanisms by which low to moderate doses of prescribed opioids were associated with increased risk of ORAEs among older patients may be complex and can possibly be explained by 2 major pathways. First, older patients may be more susceptible to opioid side effects at lower doses. While no empirical data or clinical consensus exists on a dose threshold above which opioids are considered harmful for older patients, evidence from the general population has suggested that increased risk of OD may occur at a dose as low as 20 mg/day MME [30]. The other pathway that may explain our observed associations is the use of illicit opioids, which cannot be captured with our data sources, to supplement the low-to-moderate dose of prescribed opioids. Recent reports suggest an emerging transition from prescription opioids to illicit opioids owing to increasingly restricted access to prescription opioids [3133]. It is possible that the gradual dose discontinuation group identified in this study might have sought illicit opioids to achieve pain control or to enhance euphoric effects, putting them at higher risk for ORAEs, compared to other groups of prescription opioid dose. Yet, this assumption was not supported by our data where a lower risk of ORAEs was observed in the group with gradual dose discontinuation versus other identified prescription opioid dose groups. Further studies that examine whether illicit opioid use varies across the 4 identified prescribed opioid dose trajectories are needed to assist interpretations of our study findings.

Of note is our finding that over 1 in 4 (28.3%) older patients gradually discontinued their prescription opioids in 6 months before an ORAE or matched date for controls. The opioid discontinuation was much higher in the matched controls (35.2%) than in cases (11.8%). Reasons for more controls undergoing opioid discontinuation are unclear but could be because of improved pain control, opioid ineffectiveness, or adverse opioid side effects [34]. Opioid discontinuation or dose reduction, particularly among long-term users, is among the recommendations from the 2016 CDC opioid guidance [14]. While the CDC guideline did not support abrupt tapering and sudden discontinuation of prescription opioid dose, these clinical practices were reported for patients with chronic pain and have been implicated as a contributor to unintended consequences, including ORAEs [2], OD and deaths [35], and suicidal ideation or attempts [35,36]. The US Department of Health and Human Services in 2019 issued a new clinical guide on how to appropriately reduce dose or discontinue long-term opioid analgesics, emphasizing the importance of assessing the risks and benefits of such practices [37]. In the present study, we focused on opioid-naïve older adults and found that those with gradual dose discontinuation (versus those with gradual dose increase or those with low-to-moderate dose use) had decreased risk for ORAE. It is worth noting that the gradual dose discontinuation group identified in the present study was named based on the trajectory shape, and the definition of our gradual dose discontinuation is not the same as that defined by the CDC, which involves opioid dose reduction of 10% per week after opioid use for weeks to months and 10% per month following opioid use for >1 year [37]. Whether our association findings can be seen for older adults with long-term opioid therapy requires further investigations to understand the benefits and harms of discontinuation of long-term opioid analgesics.

The present study has several noteworthy strengths. The use of a nationally representative sample of older adults who are Medicare beneficiaries from 2011 to 2018 provides population-based data that reflect current opioid prescribing practices and supplements current literature on prescription opioid dose patterns relevant to older adults at risk for ORAEs. The national data also provide a sufficient number of older adults with an incident ORAE, allowing adequate power to detect the association between trajectories of prescription opioid dose and risk for ORAE.

There are also several limitations to note. First, this study allows for establishing an association but not causation between prescription opioid dose trajectories and risk for ORAEs. Second, illicit opioid use, a growing concern in the opioid epidemic, was not captured in our data, limiting our ability to clarify the safe dose threshold of prescribed opioids for older adults. Third, our analysis of prescription dispensing data confirms receipt of medications and not medication use. Fourth, Medicare administrative claims data lack information on pain severity, which is the key factor associated with selection into opioid treatment. Fifth, while several opioid endpoints defined in claims data have been validated against medical chart review [3840], the validity of ORAE is unclear and warrants further research. Sixth, the present study did not measure the risk of adverse events associated with opioid tapering such as increased pain, insomnia, mental and physical function, and suicide. Seventh, our findings can only be generalized to Medicare fee-for-service beneficiaries with CNCP. Finally, our study excluded patients who had an incident diagnosis of ORAEs but had no prescription opioid fill during the 6 months before the diagnosis. This group may present different opioid risk profiles, and understanding risk factors beyond prescription opioid use is important to identify this high-risk subgroup of older patients.

Our findings have important clinical implications. The CDC-recommended 90 mg/day MME as the high-risk opioid dose threshold may be impractical to detect older adults at risk for ORAE. Only 5% of cases received prescribed opioid doses at or above 90 daily MME before diagnosis, leaving most cases undetected. Since 2013, the CMS required its Medicare Part D sponsors to closely monitor high-risk beneficiaries whose prescribed opioid dose was at or above 120 daily MME, and in recent years, they aligned the risky dose threshold to be consistent with the CDC-recommended 90 daily MME [41]. Prior studies have questioned the utility of using 90 daily MME in detecting patients at risk of ORAEs [21]. Echoing this finding, our study suggests that additional clinical markers to predict illicit opioid use are needed to identify older adult patients at high risk for ORAEs, particularly during the new era of increasingly restricted access to prescription opioids.

Supporting information

S1 Fig. Trajectories of mean daily MME dose prescribed in biweekly within 6 months preceding the incident diagnosis of ORAEs for cases and matched controls of older patients.

MME, morphine milligram equivalent; ORAE, opioid-related adverse event.


S2 Fig. Trajectories of mean daily MME dose prescribed in each month within 6 months preceding an incident diagnosis of opioid misuse or dependence and matched controls of older patients.

MME, morphine milligram equivalent; ORAE, opioid-related adverse event.


S3 Fig. Trajectories of mean daily MME dose prescribed in each month within 6 months preceding an incident diagnosis of opioid poisoning and matched controls of older patients.

MME, morphine milligram equivalent; ORAE, opioid-related adverse event.



  1. 1.
    Weiss AJ, Heslin KC, Barrett ML, Izar R, Bierman AS. Opioid-Related Inpatient Stays and Emergency Department Visits Among Patients Aged 65 Years and Older, 2010 and 2015. 2018. Available from:
  2. 2.
    Agency for Healthcare Research and Quality. Prevention, Diagnosis, and Management of Opioids, Opioid Misuse and Opioid Use Disorder in Older Adults 2019. Available from:
  3. 3.
    Rajbhandari-Thapa J, Zhang D, Padilla HM, Chung SR. Opioid-Related Hospitalization and Its Association With Chronic Diseases: Findings From the National Inpatient Sample, 2011–2015. Prev Chronic Dis. 2019;16:E157. pmid:31775008
  4. 4.
    Wei YJ, Chen C, Schmidt SO, LoCiganic WH, Winterstein AG. Trends in prior receipt of prescription opioid or adjuvant analgesics among patients with incident opioid use disorder or opioid-related overdose from 2006 to 2016. Drug Alcohol Depend. 2019;204:107600. pmid:31586806
  5. 5.
    Fredheim OM, Borchgrevink PC, Mahic M, Skurtveit S. A pharmacoepidemiological cohort study of subjects starting strong opioids for nonmalignant pain: a study from the Norwegian Prescription Database. Pain. 2013;154(11):2487–93. pmid:24075311
  6. 6.
    Kaplovitch E, Gomes T, Camacho X, Dhalla IA, Mamdani MM, Juurlink DN. Sex Differences in Dose Escalation and Overdose Death during Chronic Opioid Therapy: A Population-Based Cohort Study. PLoS ONE. 2015;10(8):e0134550. pmid:26291716
  7. 7.
    Henry SG, Wilsey BL, Melnikow J, Iosif AM. Dose escalation during the first year of long-term opioid therapy for chronic pain. Pain Med. 2015;16(4):733–44. pmid:25529548
  8. 8.
    Dunn KM, Saunders KW, Rutter CM, Banta-Green CJ, Merrill JO, Sullivan MD, et al. Opioid prescriptions for chronic pain and overdose: a cohort study. Ann Intern Med. 2010;152(2):85–92. pmid:20083827
  9. 9.
    Klimas J, Gorfinkel L, Fairbairn N, Amato L, Ahamad K, Nolan S, et al. Strategies to Identify Patient Risks of Prescription Opioid Addiction When Initiating Opioids for Pain: A Systematic Review. JAMA Netw Open. 2019;2(5):e193365. pmid:31050783
  10. 10.
    Gomes T, Mamdani MM, Dhalla IA, Paterson JM, Juurlink DN. Opioid dose and drug-related mortality in patients with nonmalignant pain. Arch Intern Med. 2011;171(7):686–91. pmid:21482846
  11. 11.
    Edlund MJ, Martin BC, Russo JE, DeVries A, Braden JB, Sullivan MD. The role of opioid prescription in incident opioid abuse and dependence among individuals with chronic noncancer pain: the role of opioid prescription. Clin J Pain. 2014;30(7):557–64. pmid:24281273
  12. 12.
    Garg RK, Fulton-Kehoe D, Franklin GM. Patterns of Opioid Use and Risk of Opioid Overdose Death Among Medicaid Patients. Med Care. 2017;55(7):661–8. pmid:28614178
  13. 13.
    Carey CM, Jena AB, Barnett ML. Patterns of Potential Opioid Misuse and Subsequent Adverse Outcomes in Medicare, 2008 to 2012. Ann Intern Med. 2018;168(12):837–45. pmid:29800019
  14. 14.
    Dowell D, Haegerich TM, Chou R. CDC Guideline for Prescribing Opioids for Chronic Pain—United States, 2016. JAMA. 2016;315(15):1624–45. pmid:26977696
  15. 15.
    Kroenke K, Alford DP, Argoff C, Canlas B, Covington E, Frank JW, et al. Challenges with Implementing the Centers for Disease Control and Prevention Opioid Guideline: A Consensus Panel Report. Pain Med. 2019;20(4):724–35. pmid:30690556
  16. 16.
    Haffajee RL, Mello MM, Zhang F, Zaslavsky AM, Larochelle MR, Wharam JF. Four States With Robust Prescription Drug Monitoring Programs Reduced Opioid Dosages. Health Aff (Millwood). 2018;37(6):964–74. pmid:29863921
  17. 17.
    Kertesz SG, Gordon AJ. A crisis of opioids and the limits of prescription control: United States. Addiction. 2019;114(1):169–80. pmid:30039595
  18. 18.
    Dowell D, Haegerich TM, Chou R. CDC Guideline for Prescribing Opioids for Chronic Pain—United States, 2016. MMWR Recomm Rep. 2016;65(1):1–49. pmid:26987082
  19. 19.
    Rubin R. CMS to Improve Drug Programs and Opioid Overuse Oversight. JAMA. 2018;319(12):1189. pmid:29584828
  20. 20.
    The Centers for Medicare and Medicaid Services. Additional Guidance on CY 2017 Formulary-Level Cumulative Morphine Equivalent Dose (MED) Opioid Point-of-Sale (POS) Edit 2017. Available from:
  21. 21.
    Wei YJ, Chen C, Sarayani A, Winterstein AG. Performance of the Centers for Medicare & Medicaid Services’ Opioid Overutilization Criteria for Classifying Opioid Use Disorder or Overdose. JAMA. 2019;321(6):609–11. pmid:30747958
  22. 22.
    The Centers for Medicare and Medicaid Services. Research Data Assistance Center [cited 2021 Sep 26]. Available from:
  23. 23.
    Etminan M. Pharmacoepidemiology II: the nested case-control study—a novel approach in pharmacoepidemiologic research. Pharmacotherapy. 2004;24(9):1105–9. pmid:15460170
  24. 24.
    Weiss AJ, Elixhauser A, Barrett ML, Steiner CA, Bailey MK, O’Malley L. Opioid-Related Inpatient Stays and Emergency Department Visits by State, 2009–2014: Statistical Brief #219. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville(MD); 2017. pmid:28682575
  25. 25.
    Wei YJ, Chen C, Fillingim R, Schmidt SO, Winterstein AG. Trends in prescription opioid use and dose trajectories before opioid use disorder or overdose in US adults from 2006 to 2016: A cross-sectional study. PLoS Med. 2019;16(11):e1002941. pmid:31689302
  26. 26.
    The Centers for Medicare and Medicaid Services. Opioid Oral Morphine Milligram Equivalent (MME) Conversion Factors1. 2016. Available from:
  27. 27.
    Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38. pmid:20192788
  28. 28.
    Andruff H, Carraro N, Thompson A, Gaudreau P, B. L.. Latent Class Growth Modelling: A Tutorial. Tutor Quant Methods Psychol. 2009;5(1):11–24.
  29. 29.
    Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project. HCUP Clinical Classifications Software (CCS). Available from:
  30. 30.
    Adewumi AD, Hollingworth SA, Maravilla JC, Connor JP, Alati R. Prescribed Dose of Opioids and Overdose: A Systematic Review and Meta-Analysis of Unintentional Prescription Opioid Overdose. CNS Drugs. 2018;32(2):101–16. pmid:29498021
  31. 31.
    Scholl L, Seth P, Kariisa M, Wilson N, Baldwin G. Drug and Opioid-Involved Overdose Deaths—United States, 2013–2017. MMWR Morb Mortal Wkly Rep. 2018;67(5152):1419–27. pmid:30605448
  32. 32.
    Seth P, Rudd RA, Noonan RK, Haegerich TM. Quantifying the Epidemic of Prescription Opioid Overdose Deaths. Am J Public Health. 2018;108(4):500–2. pmid:29513577
  33. 33.
    Compton WM, Jones CM, Baldwin GT. Relationship between Nonmedical Prescription-Opioid Use and Heroin Use. N Engl J Med. 2016;374(2):154–63. pmid:26760086
  34. 34.
    Husain JM, LaRochelle M, Keosaian J, Xuan Z, Lasser KE, Liebschutz JM. Reasons for Opioid Discontinuation and Unintended Consequences Following Opioid Discontinuation Within the TOPCARE Trial. Pain Med. 2019;20(7):1330–7. pmid:29955866
  35. 35.
    Oliva EM, Bowe T, Manhapra A, Kertesz S, Hah JM, Henderson P, et al. Associations between stopping prescriptions for opioids, length of opioid treatment, and overdose or suicide deaths in US veterans: observational evaluation. BMJ. 2020;368:m283. pmid:32131996
  36. 36.
    Demidenko MI, Dobscha SK, Morasco BJ, Meath THA, Ilgen MA, Lovejoy TI. Suicidal ideation and suicidal self-directed violence following clinician-initiated prescription opioid discontinuation among long-term opioid users. Gen Hosp Psychiatry. 2017;47:29–35. pmid:28807135
  37. 37.
    Dowell D, Haegerich T, Chou R. No Shortcuts to Safer Opioid Prescribing. N Engl J Med. 2019;380(24):2285–7. pmid:31018066
  38. 38.
    Green CA, Hazlehurst B, Brandes J, Sapp DS, Janoff SL, Coplan PM, et al. Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data. Pharmacoepidemiol Drug Saf. 2019;28(8):1138–42. pmid:31095831
  39. 39.
    Green CA, Perrin NA, Janoff SL, Campbell CI, Chilcoat HD, Coplan PM. Assessing the accuracy of opioid overdose and poisoning codes in diagnostic information from electronic health records, claims data, and death records. Pharmacoepidemiol Drug Saf. 2017;26(5):509–17. pmid:28074520
  40. 40.
    Green CA, Perrin NA, Hazlehurst B, Janoff SL, DeVeaugh-Geiss A, Carrell DS, et al. Identifying and classifying opioid-related overdoses: A validation study. Pharmacoepidemiol Drug Saf. 2019;28(8):1127–37. pmid:31020755
  41. 41.
    The Centers for Medicare and Medicaid Services. Opioid Misuse Strategy. 2016. Available from:
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Edmonton-area doctors call on province to provide more data on opioid poisoning events

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Edmonton-area doctors are calling on the province to release more localized data on overdoses to better address the drug poisoning crisis.

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In a statement Monday, the Edmonton Zone Medical Staff Association’s Opioid Poisoning Committee (OPC) said they have twice requested the release of local geographical area data for opioid poisoning-related deaths and calls made to emergency medical services on the Alberta’s Substance Use Surveillance System (ASUSS) dashboard.

Between January and October of 2021, there have been 1,372 deaths related to drug poisoning in the province, the latest data shows, and there were 153 EMS responses to opioid-related events between Jan. 24 to Jan. 30.

It is imperative that this information be added to the publicly facing dashboard, and by extension that it be provided in a timely manner,” the OPC states.

“This information helps mobilize the resources and efforts in the communities to reduce incidents of harm and death, ensuring that those working on the front lines of this effort can be where they need to be. The information would not impact the privacy of Albertans but would identify and support neighbourhoods that are being most impacted by this crisis.”

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Letters were sent to Health Minister Jason Copping in December and associate minister of mental health and addictions Mike Ellis in January requesting the data, but there has been no response, the OPC said.

Ellis’ press secretary Eric Engler said in a statement the ASUSS is the most comprehensive reporting tool in the country “with respect to addiction-related harms.”

“We continually work to enhance the data available on ASUSS, for example, the recently added ‘location of opioid deaths’ tab,” Engler said.

The location of deaths tab provides statistics on the number of deaths that occurred in private residences lived in and owned by the individual, private residences owned by another individual, the public, hotels, and other facilities. In Q3 2021, 51 per cent of opioid poisonings occurred in private residences owned by the individual who died.

The last time the province publicly provided neighbourhood-level data was in the Q2 2020 opioid surveillance report . Engler did not answer questions on why neighbourhood-level reporting has not been provided since then.

“We will be happy to work with individual stakeholder groups who provide outreach services and help them to focus their efforts,” Engler said.