Abstract
Background
High daily and total doses of opioid analgesics (OA) increase the risk for drug overdose and may be risks for all-cause hospitalization.
Objective
To examine the association of OA dose measures with future all-cause hospitalization.
Design/Patients
Cohort study of 87,688 national HMO enrollees aged 45-64 with non-cancer pain who filled ≥2 OA prescriptions from 1/2009-7/2012.
Methods
Outcomes were all-cause hospitalization and hospital days in 6-month intervals after first OA filled. In generalized linear mixed models, we examined interactions of 5 daily OA dose categories and 5 total dose categories in each 6-month interval adjusted for demographics, clinical conditions, psychotropic drugs, and current hospitalization. For high total OA doses, percentage of days covered by OA prescriptions in 6-months was examined.
Results
Over 3 years, an average of 12% of subjects were hospitalized yearly for a mean 6.5 (SD=8.5) days. Compared with no OAs, adjusted odds of future hospitalization for high total opioid dose (>1830 mg) were 35 to 44% greater depending on daily dose categories (all p<0.05) but total OA dose ≤1830 mg had weak or no association with future hospitalization regardless of daily OA dose. For high total OA doses, odds of hospitalization were 41 to 51% greater for categories of % time on OAs above >50% (3 months) versus no OAs (all p<0.05). Similar effects were observed for hospital days.
Conclusions
Higher total OA doses of > 3 months within a 6-month period significantly increased the risk for all-cause hospitalization and longer inpatient stays in the next 6 months.
Keywords: Analgesics Opioid, Hospitalization, Electronic Medical Record, Pain Management
Introduction
Longer term and higher doses of opioid analgesics have been associated with multiple adverse outcomes such as loss of work, cognitive decline, and poor function.1-4 One of the most widely reported complications of opioid therapy is drug overdose.5-9 In population-based studies, daily morphine equivalent doses >100 mg have been associated with significantly increased risk of drug overdose. 5-10 Among health maintenance organization (HMO) enrollees filling at least two prescriptions for opioids, our group reported that daily opioid doses ≥100 mg were associated with approximately three-fold greater adjusted odds of drug overdose. 10 We also observed over a two-fold increase in odds of drug overdose for lower daily doses of 50-99 mg if the patient also received a high total opioid dose (>1830 mg) over a 6-month period. This analysis suggests that clinicians may need to monitor not only daily dose but also total dose of opioids to reduce the risk of drug overdose.
Yet drug overdose represents only a small subset of all hospitalizations for persons receiving long-term or higher doses of opioid for non-cancer pain. These patients have significant demand for urgent care services including hospitalization for diverse reasons such as adverse effects of opioids, underlying cause of chronic pain, and comorbidities such as mental health disorders. 11 In a cohort of elderly primary care patients who were high hospital utilizers, Freund and colleagues reported that chronic pain and depression were the most common conditions co-occurring with their other comorbidities. 12 However, little is known about the association of opioid dose with the risk of all-cause hospitalization for patients with non-cancer pain.
In this paper, we examined hospitalizations for a national cohort of HMO enrollees with non-cancer pain who filled at least two prescriptions for Schedule II or III opioids over a 3.5 year timeframe. This retrospective cohort analysis aims to identify clinically useful opioid dose measures for clinicians, administrators, and policymakers to use in identifying patients at increased risk of future hospitalization and to develop interventions to reduce this risk.
Methods
Study sample
From Aetna administrative databases including enrollment files and paid claims for services, we identified 261,528 subjects aged 18 to 64 years who had at least two paid claims for Schedule II or III non-injectable opioid analgesic prescriptions from 01/2009 through 07/2012. 10 For individuals meeting these criteria, study cohort eligibility required at least 12 months of enrollment and complete data on demographics and OA prescriptions as well as clinical conditions from at least one encounter (Appendix 1). 10 We excluded subjects with a cancer diagnosis who have high hospital utilization and those younger than 45 years because of a higher likelihood of pregnancy-related hospitalization. To afford sufficient observation time for outcomes, subjects with < 12 months follow-up after the first opioid prescription were excluded. The resultant study cohort totaled 87,688 subjects.
To capture the changing nature of medication utilization and clinical conditions in this longitudinal study, we divided the study timeframe into 6-month intervals starting with the first opioid prescription and ending with the subject's last enrollment or end of the study (Appendix 2). Six-month intervals were studied because this is the maximum duration of benefit from randomized trials of opioid therapy for non-cancer pain.13 This study was approved by the University of Texas Health Science Center at San Antonio's Institutional Review Board.
Outcome variables
Study outcomes were all-cause hospitalization (binary) and hospital days (discrete) per 6-month interval and were measured repeatedly for up to six 6-month intervals.
Primary independent variables
We examined two opioid dose measures within a 6-month interval and hospitalization outcomes in the next 6 months (Appendix 2). We did not examine OA use in the last 6 months of the study timeframe because subsequent hospitalization outcomes were not available. We defined the total morphine equivalent dose of opioid analgesic prescriptions filled within a 6-month interval based on the method used by Edlund et al. 14 and adapted by our group. 10, We also defined the daily dose of opioid analgesics that is a widely used metric used in chronic pain management guidelines. 10,15
To calculate the total opioid dose, all filled Schedule II or III opioid analgesic prescriptions (non-injectable formulations) were identified from claims for filled prescriptions for each 6-month interval. The morphine equivalent dose for each opioid prescription was calculated from the number of pills dispensed multiplied by strength (in milligrams) and by a morphine equivalent conversion factor derived from several sources including published data,16,17 conversion tables from Internet sources, and drug information resources.18,19 A clinical pharmacist reviewed and finalized conversions. When an opioid prescription spanned two 6-month intervals, the dose was divided proportionate to time in each interval. The total dose for all opioid prescriptions within an interval was summed and categorized by quartile of non-zero total dose as: 1-190, 191-450, 451-1830, and >1830 mg. 10
To calculate the daily opioid dose in each interval, the total dose was divided by total non-overlapping days’ supply covered by all prescriptions. The average daily dose was categorized as in other studies: 1-19, 20-49, 50-99, and ≥100 mg. 5,6,10 In each 6-month interval, the percentage of days covered by filled prescriptions was calculated as total days’ supply/180.
Other independent variables
Demographic data included age as of July 2012, sex, and U.S. region. From available diagnosis codes for encounters, pain-related conditions were identified including: back pain, other osteoarthritis, neuropathic pain, chronic pain unspecified, or chronic headache (ICD-9-CM codes available from authors). Mental health/substance use disorders were similarly identified: anxiety or post-traumatic stress disorder (PTSD), depression, psychosis, drug abuse, and alcohol abuse. Once a psychiatric condition or substance use disorder was diagnosed, it was considered to persist because these are usually not transient. We examined filled prescriptions for psychoactive drugs in 6 month intervals including: benzodiazepines (i.e., clonazepam, alprazolam, lorazepam, diazepam, chlordiazepine, temazepam, flurazepam), antidepressants (i.e., SSRIs, SNRIs, tricyclics -- complete list available from authors), and sedatives (i.e., zolpidem, eszopiclone). For these drugs, time-varying variables were created as follows: benzodiazepines (0, 1-30, 31-90, 91-180 days), sedatives (0, 1-30, 31-90, 91-180 days), and antidepressants (0, 1-60, 61-180 days). Categories for duration of antidepressants differed because a clinical response can take up to 6 to 8 weeks.
Statistical analyses
Descriptive statistics were examined for study cohort characteristics. For the binary all-cause hospitalization outcome, repeated measures logistic regression models were estimated using generalized estimating equations (GEE) to examine associations of daily opioid dose, total opioid dose, and their interaction with all-cause hospitalization. The fully adjusted model includes demographics, chronic pain conditions, mental health conditions, substance use disorders, other psychoactive drugs and current hospitalization (Yes/No). For the hospital days per 6-month outcome, a series of repeated measures Poisson regressions were estimated using the GEE approach.
In a post-hoc sensitivity analysis, we examined the association of the percentage of days covered by prescribed opioids, categorized based on approximate quartiles and clinical judgment, with hospitalization among subjects with a high total dose (>1830 mg). For this analysis, we created a composite measure of opioid treatment for each 6-month interval that has six categories: 1) none, 2) low total dose 1-1830 mg, 3) high total dose >1830 mg with ≤50% of days on opioids, 4) total dose >1830 mg with >50% to 75% of days on opioids, 5) total dose >1830 mg with >75% to 90% of days on opioids, and 6) total dose >1830 mg and >90% of days on opioids. Adjusted regression analyses described above were repeated for both outcomes and include this composite measure. All statistical tests were performed with a 2-sided significance level of 0.05 and analyses were conducted using SAS (Version 9.3).
Results
Of 87,688 study subjects, 54.8% were women and the mean age was 53.8 years (SD=5.5). Nearly half of the cohort resided in Southern states (Table 1). In the baseline 6-month interval, the most common chronic non-cancer pain conditions were musculoskeletal involving large joint arthritis/arthralgia (38.4%) and back pain (28.2%). In regard to mental health and substance use conditions, both anxiety/PTSD and depression were diagnosed in approximately 7% of the cohort, while psychosis, alcohol and other substance use disorders were each diagnosed in less than 2%. In the baseline interval, 12.7% of subjects were hospitalized. The majority of patients received a daily dose of 20 to 49 mg and the median total dose was 450 mg. The median percent time exposed to opioids was 6.7% among all study subjects and 70% for those with a high total dose (>1830 mg).
Table 1.
Patient Characteristics at Baseline*
| Characteristics | Total (N=87,688) |
|---|---|
| Demographics | |
| Women, n (%) | 48077 (54.8) |
| Age, mean (SD) | 53.8 (5.5) |
| U.S. Region, n (%) | |
| Midwest | 4609 (5.3) |
| Northeast | 27568 (31.4) |
| South | 40767 (46.5) |
| West | 14744 (16.8) |
| Clinical Conditions†, n (%) | |
| Non-cancer pain conditions | |
| Back pain | 24767 (28.2) |
| Large joint arthritis, other musculoskeletal‡ | 33689 (38.4) |
| Neuropathy | 1519 (1.7) |
| Chronic pain (unspecified) | 3229 (3.7) |
| Headache | 2837 (3.2) |
| Mental health and substance use disorders | |
| Anxiety or post-traumatic stress disorder | 6006 (6.9) |
| Depression | 6111 (7.0) |
| Psychosis | 1259 (1.4) |
| Alcohol abuse | 877 (1.0) |
| Other substance abuse | 615 (0.7) |
| Current hospitalization, n (%) | 11165 (12.7) |
| Opioid Measures, n (%) | |
| Daily MED dose, mg | |
| 0 | - |
| 1-19 | 9870 (11.3) |
| 20-49 | 50050 (57.1) |
| 50-99 | 21188 (24.2) |
| >=100 | 6580 (7.5) |
| Total MED dose, mg | |
| 0 | - |
| 1-190 | 20276 (23.1) |
| 191-450 | 26000 (29.7) |
| 451-1830 | 23551 (26.9) |
| >1830 | 17861 (20.4) |
| Percent time exposed to opioid therapy, Median (Q1, Q3) | |
| Among any total MED | 6.7 (2.8, 22.2) |
| Among total MED >1830 mg | 70 (42.8, 93.9) |
The first 6-month interval started with the date of the first opioid prescription
Clinical conditions diagnosed at the baseline 6-month interval. ICD-9-CM codes available from authors.
Arthritis, arthralgia, fracture, sprains
In the three study years, an average of 12% of the cohort was hospitalized yearly (Table 2), or 1,120 hospitalizations per 10,000 person-years. Among those who were hospitalized, inpatient days averaged 6.5 (SD=8.5). The highest proportion of hospitalized subjects was 6.5%, occurring in the 6-month interval immediately following the first opioid treatment interval. In subsequent 6-month intervals, hospitalization rates were relatively stable, ranging from 5.2% to 6.1% (Table 2). As shown, future hospitalization rates increased monotonically with increasing total or daily dose within each 6-month interval.
Table 2.
Opioid Dose Measures and Proportion of Hospitalized Subjects in Next Six-Month Interval
| 6-Month Interval | ||||||
|---|---|---|---|---|---|---|
| Subjects (N) | 1 (Baseline) N=87,688 | 2 N=65,835 | 3 N=46,041 | 4 N=31,550 | 5 N=18,915 | 6 N=3,502 |
| Overall (%) | 6.5 | 5.9 | 5.9 | 5.4 | 5.2 | 6.1 |
| Opioid dose measure* | ||||||
| Daily dose (%) | ||||||
| 0 mg | - | 4.8 | 4.4 | 4.0 | 3.6 | 3.2 |
| 1-19 mg | 5.9 | 5.6 | 6.0 | 5.6 | 5.6 | 4.4 |
| 20-49 mg | 6.2 | 6.5 | 7.1 | 6.6 | 6.1 | 6.1 |
| 50-99 mg | 6.8 | 7.9 | 7.5 | 7.6 | 7.6 | 9.8 |
| >=100 mg | 9.0 | 9.3 | 10.3 | 9.2 | 9.5 | 9.5 |
| Total dose (%)* | ||||||
| 0 mg | - | 4.8 | 4.4 | 4.0 | 3.6 | 3.2 |
| 1-190 mg | 5.5 | 4.7 | 5.0 | 4.1 | 4.0 | 2.7 |
| 191-450 mg | 5.1 | 5.1 | 6.3 | 6.7 | 5.0 | 3.2 |
| 451-1830 mg | 6.5 | 7.4 | 7.9 | 7.2 | 7.1 | 7.0 |
| >1830 mg | 9.8 | 9.6 | 9.6 | 8.9 | 8.8 | 9.0 |
Quartiles for total dose among opioid users
Entries are percent of future hospitalizations. For example, 6.5% (= 5704/87688) patients at baseline were hospitalized in the subsequent 6-month interval.
In unadjusted analyses, a significant interaction between daily dose and total dose (p<0.001) revealed that, within each daily dose category, the odds of hospitalization differed by total dose (all p<0.05, Table 3). When the total dose was >1830 mg, the odds of future hospitalization rose monotonically with increasing daily dose (i.e., <20, 20-49, 50-99, ≥100mg): 1.33, 1.84, 1.96, and 2.08 (p<0.05 for all comparisons vs. no opioids). On the other hand, when the total dose was 450 or less, all daily dose categories including a very high daily dose (≥100mg) were not associated with future hospitalization (all p>0.05 vs. no opioids). When the total dose was 451 to 1830mg, a non-linear association with hospitalization appeared with higher odds for lower daily doses. For the outcome of hospital days per 6-month, increasing daily dose was also associated with more hospital days per 6-month when the total dose was high (>1830mg), whereas for lower total doses, daily dose was weakly positive or even protective versus no opioids.
Table 3.
Unadjusted Associations of the Interaction of Total Opioid Dose and Daily Dose with Hospitalization Outcomes
| All-cause hospitalization (Yes/No), Odds Ratio (95% CI) | |||||
|---|---|---|---|---|---|
| Daily Morphine Equivalent Dose (mg) | |||||
| Total Morphine Equivalent Dose (mg) | 0 | 1-19 | 20-49 | 50-99 | >=100 |
| 0 (reference) | 1 | - | - | - | - |
| 1-190 | - | 1.06 (0.95, 1.19) | 1.01 (0.95, 1.08) | 1.07 (0.95, 1.19) | 0.73 (0.44, 1.21) |
| 191-450 | - | 1.08 (0.96, 1.22) | 1.03 (0.96, 1.10) | 0.99 (0.9, 1.10) | 0.88 (0.67, 1.15) |
| 451-1830 | - | 1.34 (1.21, 1.48)* | 1.37 (1.28, 1.46)* | 1.16 (1.05, 1.27)* | 1.25 (0.98, 1.59) |
| >1830 | - | 1.33 (1.09, 1.62)* | 1.84 (1.73, 1.97)* | 1.96 (1.82, 2.11)* | 2.08 (1.93, 2.24)* |
| Hospital days per 6-month, Incident Rate Ratio (95% CI) | |||||
|---|---|---|---|---|---|
| Daily Morphine Equivalent Dose (mg) | |||||
| Total Morphine Equivalent Dose (mg) | 0 | 1-19 | 20-49 | 50-99 | >=100 |
| 0 (reference) | 1 | - | - | - | - |
| 1-190 | - | 0.95 (0.79, 1.14) | 0.90 (0.82, 0.99)* | 1.03 (0.87, 1.23) | 0.63 (0.36, 1.12) |
| 191-450 | - | 0.92 (0.77, 1.10) | 0.93 (0.84, 1.02) | 0.79 (0.69, 0.91)* | 0.69 (0.49, 0.98)* |
| 451-1830 | - | 1.31 (1.10, 1.57)* | 1.26 (1.13, 1.40)* | 1.01 (0.86, 1.19) | 0.99 (0.71, 1.37) |
| >1830 | - | 1.32 (0.93, 1.89) | 1.79 (1.60, 2.01)* | 1.76 (1.54, 2.01)* | 2.09 (1.85, 2.36)* |
p<0.05 compared with no opioid therapy
Note: Logistic regression was used for all-cause hospitalization and odds ratios (95% CI) were reported with no opioid therapy as the reference group. Poisson regression was used for hospital days per 6-month and incident rate ratio (95% CI) reported with the no opioid therapy as the reference.
In the model adjusting for all covariates (Table 4), the interaction between total dose and daily dose was also significant (p=0.002). When the total dose was high (>1830mg), the adjusted odds of future hospitalization were significantly increased by 35 to 44% for daily doses of 20-49mg or greater versus no opioids (p<0.05 for all comparisons). When the total dose was <1830 mg, the majority of daily dose categories were not significantly associated with hospitalization. Similarly, in the fully adjusted analysis of hospital days, the number of inpatient days were increased by 28 to 48% when the total dose was >1830mg and daily dose was >20 mg, but these associations were non-significant or protective when the total dose was lower.
Table 4.
Adjusted Association for the Interaction of Total Opioid Dose and Daily Dose with Hospitalization Outcomes*
| All-cause hospitalization (Yes/No), Odds Ratio (95% CI) | |||||
|---|---|---|---|---|---|
| Daily Morphine Equivalent Dose (mg) | |||||
| Total Morphine Equivalent Dose (mg) | 0 | 1-19 | 20-49 | 50-99 | >=100 |
| 0 | 1 | - | - | - | - |
| 1-190 | - | 1.09 (0.97, 1.23) | 1.07 (1.00, 1.14) | 1.12 (1.00, 1.26)† | 0.75 (0.45, 1.23) |
| 191-450 | - | 1.00 (0.88, 1.13) | 0.99 (0.92, 1.06) | 0.97 (0.88, 1.08) | 0.87 (0.68, 1.12) |
| 451-1830 | - | 1.16 (1.04, 1.29)† | 1.14 (1.07, 1.22)† | 0.94 (0.85, 1.03) | 1.08 (0.85, 1.35) |
| >1830 | - | 1.10 (0.90, 1.34) | 1.41 (1.32, 1.51)† | 1.35 (1.25, 1.46)† | 1.44 (1.34, 1.55)† |
| Hospital days per 6-month, Incident Rate Ratio (95% CI) | |||||
|---|---|---|---|---|---|
| Daily Morphine Equivalent Dose (mg) | |||||
| Total Morphine Equivalent Dose (mg) | 0 | 1-19 | 20-49 | 50-99 | >=100 |
| 0 | 1 | - | - | - | - |
| 1-190 | - | 0.97 (0.8, 1.18) | 0.94 (0.85, 1.04) | 1.06 (0.88, 1.27) | 0.60 (0.33, 1.1) |
| 191-450 | - | 0.85 (0.71, 1.02) | 0.88 (0.79, 0.98)† | 0.75 (0.65, 0.86)† | 0.65 (0.46, 0.92)† |
| 451-1830 | - | 1.16 (0.97, 1.4) | 1.09 (0.97, 1.22) | 0.83 (0.71, 0.98)† | 0.81 (0.59, 1.13) |
| >1830 | - | 1.12 (0.77, 1.63) | 1.41 (1.25, 1.58)† | 1.28 (1.12, 1.46)† | 1.48 (1.29, 1.69)† |
Adjusted for time interval, age, gender, region, 5 non-cancer pain condition indicators, anxiety, depression, psychotic disorder, alcohol abuse, substance abuse, duration of antidepressants per 6-month interval (3 levels: none, 1-60d, 61-180d), duration of benzodiazepines per 6-month interval (4 levels: none, 1-30d, 31-90d, 91-180d), duration of sedatives per 6-month interval (4 levels: none, 1-30d, 31-90d, 91-180d) and current hospitalization.
p<0.05 compared with no opioid therapy
In a sensitivity analysis, we examined the percentage of days covered by filled opioid prescriptions within a 6-month interval for subjects receiving high dose therapy (Table 5). Compared with no opioid therapy, the adjusted odds of future hospitalization were 5% greater for low total opioid dose (1-1830mg) and 21% greater for high total dose (>1830mg) when the duration of treatment was shorter (≤50% of the 6-month interval). However, the odds were increased by 40 to 51% for a high total dose (>1830mg) with longer periods of treatment (>50% of the interval). For hospital days as the outcome, subjects with high total doses (>1830mg) and longer periods of treatment (>50% of the interval) had 41 to 71% more hospital days per 6-months than those with no opioid therapy.
Table 5.
Adjusted Associations of Opioid Analgesic Dose and Duration with Hospitalization Outcomes*
| Opioid Analgesic Category | All-cause hospitalization Odds Ratio (95% CI) | Hospital days per 6-month Incident Rate Ratio (95% CI) |
|---|---|---|
| 0 mg | 1 | 1 |
| 1-1830 mg | 1.05 (1.00 ,1.10)† | 0.94 (0.87 ,1.01) |
| >1830 mg & pct days on opioid <=50% | 1.21 (1.11 ,1.31)† | 1.10 (0.96 ,1.26) |
| >1830 mg & pct days on opioid >50%, <= 75% | 1.51 (1.40 ,1.64)† | 1.45 (1.26 ,1.67)† |
| >1830 mg & pct days on opioid >75%, <= 90% | 1.50 (1.38 ,1.64)† | 1.71 (1.46 ,1.99)† |
| >1830 mg & pct days on opioid >90% | 1.41 (1.31 ,1.52)† | 1.41 (1.26 ,1.58)† |
Adjusted for time interval, age, gender, region, 5 non-cancer pain condition indicators, anxiety, depression, psychotic disorder, alcohol abuse, substance abuse, duration of antidepressants per 6-month interval (3 levels: none, 1-60d, 61-180d), duration of benzodiazepines per 6-month interval (4 levels: none, 1-30d, 31-90d, 91-180d), duration of sedatives per 6-month interval (4 levels: none, 1-30d, 31-90d, 91-180d) and current hospitalization.
p<0.05 compared with no opioid therapy
Discussion
In a national cohort of HMO enrollees who filled at least two prescriptions for opioid analgesics, 12% were hospitalized annually. Other studies of opioid users have focused on only a fraction of these hospitalizations. For example, a recent AHRQ study reported that the rate of hospitalization for complications from accidental or deliberate overuse of opioids more than doubled from 11.7/10,000 in 1993 to 29.5/10,000 in 2010. 20 However, in our cohort, the all-cause hospitalization rate was 1120 per 10,000 person-years or over 40 times greater than the rate for complications from overuse of opioids. By comparison, hospitalization for heart failure was only 32.8/10,000 nationally in 2010. 21 Thus, our study confirms the significant demand for hospital care by patients treated with opioids. A novel finding of our study is that the total dose of prescriptions filled over 6 months is significantly associated with an increased risk of future hospitalization. When the total dose within 6 months was in the top quartile (>1830 mg in our cohort), the adjusted odds of future hospitalization ranged from 35 to 44% greater than no opioids for daily opioid doses above 20 mg/day. On the other hand, when the total dose was ≤1830 mg, the daily opioid dose was only weakly associated with future hospitalization. These associations were similar for hospital days per 6-months as the outcome.
Edlund and colleagues examined the total dose of opioids in a national cohort of veterans with chronic non-cancer pain who filled at least one opioid prescription.22 In 2011, the 60th percentile for the total opioid dose for these veterans was 3610 mg within a year which is roughly equivalent to our top quartile (1830 mg) over a 6-month interval. These data support replicating our study in veterans to evaluate whether a similarly increased risk of hospitalization appears for those with high total opioid doses. In support of a concern among veterans, a population-based, cross-sectional study of hospitalized veterans reported a high rate of chronic opioid therapy (≥90 days) in the 6 months prior to hospitalization.23
Other studies have reported increased risk of hospitalization with chronic opioid therapy. Among 1,045 patients followed up to one year post-transplantation, long-term opioids were associated with up to six-fold greater risk of at least four admissions within that year.24 Among 13,127 Danish adults on opioid therapy, the odds of future hospitalization from injuries were increased by 74% for long-term therapy and 46% for short-term therapy versus no opioids and by 3-fold and 1.6-fold, respectively, for hospitalization due to toxicity/poisoning. 9 However, none of these studies examined the dose of opioids.
In a sensitivity analysis, we found that when a subject received a high total opioid dose within 6 months, treatment for more than 50% of the interval (i.e., >3 months) was associated with significantly increased risk of future hospitalization and significantly more hospital days. Because the strongest evidence for the benefit of opioids for chronic non-cancer pain comes from trials of less than 3 months,25 these data lend additional support to recommendations to minimize both dose and duration of opioid therapy.
Our study has several limitations. First, we did not assess the immediate risk of hospitalization after starting opioid therapy. Second, our outcome of hospitalization represents only one measure of risk. Thus, our data should not be regarded as supporting short term use of high dose opioids over 100 to 120 mg per day.26 In an earlier study, we reported that either high daily dose (≥100 mg) or moderately high daily dose (50-99 mg) plus a high total dose (>1830) increased the risk of drug overdose.10 Third, we could not examine the reason for hospitalizations in this analysis. Therefore, we cannot presume that opioid therapy caused these hospitalizations but it serves as a proxy for other factors such as disability and mental health disorders that increase risk of hospitalization. However, we did adjust for pain conditions as well as mental health and substance abuse disorders that are known to increase the risk of hospitalization in other cohorts.27-30 In a national veterans study, the most common clinical conditions associated with long-term opioid therapy were major depression and post-traumatic stress disorder. 22 Lastly, we did also not consider the number of prescribers of opioids. In a Medicare study, one versus four prescribers of opioid analgesics increased patients’ annual hospitalization rate from 1.6% to 4.8%, respectively. 31
Although the total opioid dose categories observed for our study population may differ from those in other cohorts, these data offer additional evidence for clinicians to consider this measure when assessing risk for hospitalization and, among subjects on high doses, the percentage of time on opioids offer an additional measure of risk. Because opioid users with non-cancer pain are heavy consumers of health care services, 32,33 public health benefits and reductions in costs of care may be substantial if opportunities can be identified to reduce hospital utilization by persons treated with higher doses of opioid analgesics.
Key points.
The total opioid analgesic dose from all filled prescriptions per 6-month interval by persons with non-cancer pain was more strongly associated with all-cause hospitalization in the next 6 months than daily dose. Significant associations were observed for patients with high total doses > 1830 mg and, within this group, patients who filled prescriptions for more than 3 months had the highest risks for future hospitalization. Total opioid dose and percent time on opioids may offer valuable metrics for assessing risk for inpatient care.
Acknowledgments
Sponsors
The work on this project was supported by an intramural grant from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 1UL TR001120. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Appendix
Appendix 1.
Derivation of Study Cohort of HMO Enrollees Filling at 2 Prescriptions for Schedule II or III Opioid Analgesics
| Patient N | ||
|---|---|---|
| Schedule II or III opioid analgesic prescription filled from 01/2009 to 07/2012 | 390,251 | |
| Exclusions: | ↓ | |
| Enrolled < 12 months | (N=3 9,923) | 350.328 |
| ↓ | ||
| Missing demographic data | (N=10) | 350.318 |
| ↓ | ||
| Age<18 or>64 yr | (N= 9,130) | 341,188 |
| ↓ | ||
| Incomplete data on opioid analgesic prescriptions | (N=20,394) | 320,794 |
| ↓ | ||
| <2 Schedule II or III opioid analgesic prescript ions | (N=41,819) | 278,975 |
| ↓ | ||
| Missing diagnosis data | (N=17,447) | 261,528 |
| ↓ | ||
| Cancer diagnosis | (N=26,165) | 235,363 |
| ↓ | ||
| Opioid abuse/dependence diagnosis prescribed methadone or buprenorphine | (N=1,771) | 233.592 |
| ↓ | ||
| Incomplete prescribing data | (N=12,603) | 220.989 |
| ↓ | ||
| < 6 months enrollment after the first opioid analgesic prescription | (N=14,120) | 206.869 |
| ↓ | ||
| Age <45 yr (pregnancy-related hospitalization) | (N=97,577) | 109.292 |
| ↓ | ||
| <12 months follow-up after the first opioid analgesic prescription | (N=21,604) | 87,688 |
Appendix 2.
Calculation of Time-Varying Covariates and Repeated Measures of Outcomes
 |
Appendix 3.
Proportions of Subjects in Daily and Total Opioid Dose Categories by 6-Month Interval
| 6-Month Interval | ||||||
|---|---|---|---|---|---|---|
| N Subjects | 1 (baseline) N=87,688 | 2 N=65,835 | 3 N=46,041 | 4 N=31,550 | 5 N=18,915 | 6 N=3,502 |
| Opioid Dose Measures* | ||||||
| Daily dose (%) | ||||||
| 0 mg | 0 | 49.6 | 52.7 | 52.7 | 50.8 | 36.0 |
| 1-19 mg | 11.3 | 6.8 | 6.4 | 6.3 | 6.3 | 6.5 |
| 20-49 mg | 57.1 | 26.6 | 24.9 | 24.9 | 25.1 | 25.7 |
| 50-99 mg | 24.2 | 11.1 | 10.3 | 9.8 | 10.3 | 14.6 |
| >=100 mg | 7.5 | 6.0 | 5.8 | 6.3 | 7.5 | 17.2 |
| Total dose (%)† | ||||||
| 0 mg | 0 | 49.6 | 52.7 | 52.7 | 50.8 | 36.0 |
| 1-190 mg | 23.1 | 12.0 | 11.4 | 10.7 | 9.2 | 5.3 |
| 191-450 mg | 29.7 | 10.4 | 9.8 | 9.3 | 8.6 | 5.3 |
| 451-1830 mg | 26.9 | 11.2 | 10.1 | 10.3 | 10.6 | 10.7 |
| >1830 mg | 20.4 | 16.8 | 16.1 | 16.9 | 20.9 | 42.8 |
Morphine equivalent units
Quartiles among opioid users
Entries are percent of subjects. For example, at the baseline interval, 11.3% (i.e., 9870 out of 87688) patients had a daily MED dose of 1-19 mg.
Footnotes
Conflict of Interest: None
References
- 1.Kidner CL, Mayer TG, Gatchel RJ. Higher opioid doses predict poorer functional outcome in patients with chronic disabling occupational musculoskeletal disorders. J Bone Joint Surg Am. 2009;91:919–27. doi: 10.2106/JBJS.H.00286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Volinn E, Fargo JD, Fine PG. Opioid therapy for nonspecific low back pain and the outcome of chronic work loss. Pain. 2009;142:194–201. doi: 10.1016/j.pain.2008.12.017. [DOI] [PubMed] [Google Scholar]
- 3.Solomon DH, Rassen JA, Glynn RJ, Lee J, Levin R, Schneeweiss S. The comparative safety of analgesics in older adults with arthritis. Arch Intern Med. 2010;170:1968–76. doi: 10.1001/archinternmed.2010.391. [DOI] [PubMed] [Google Scholar]
- 4.Schiltenwolf M, Akbar M, Hug A, et al. Evidence of specific cognitive deficits in patients with chronic low back pain under long-term substitution treatment of opioids. Pain Physician. 2014;17:9–20. [PubMed] [Google Scholar]
- 5.Dunn KM, Saunders KW, Rutter CM, et al. Opioid prescriptions for chronic pain and overdose: A cohort study. Ann Intern Med. 2010;152:85–92. doi: 10.1059/0003-4819-152-2-201001190-00006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Bohnert AS, Valenstein M, Bair MJ, et al. Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA. 2011;305:1315–21. doi: 10.1001/jama.2011.370. [DOI] [PubMed] [Google Scholar]
- 7.Gomes T, Juurlink DN, Dhalla IA, Mailis-Gagnon A, Paterson JM, Mamdani MM. Trends in opioid use and dosing among socio-economically disadvantaged patients. Open Med. 2011;5:e13–22. [PMC free article] [PubMed] [Google Scholar]
- 8.Paulozzi LJ, Kilbourne EM, Shah NG, et al. A history of being prescribed controlled substances and risk of drug overdose death. Pain Med. 2012;13:87–95. doi: 10.1111/j.1526-4637.2011.01260.x. [DOI] [PubMed] [Google Scholar]
- 9.Ekholm O, Kurita GP, Hojsted J, Juel K, Sjogren P. Chronic pain, opioid prescriptions and mortality in Denmark: A population-based cohort study. Pain. 2014;155:2486–90. doi: 10.1016/j.pain.2014.07.006. [DOI] [PubMed] [Google Scholar]
- 10.Liang Y, Turner BJ. Assessing risk for drug overdose in a national cohort: Role for both daily and total opioid dose? J Pain. 2014 doi: 10.1016/j.jpain.2014.11.007. doi: 10.1016/j.jpain.2014.11.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education [December 12, 2014];Relieving pain in America: A blueprint for transforming prevention, care, education, and research. Available at: http://www.ncbi.nlm.nih.gov/books/NBK91497/. [PubMed]
- 12.Freund T, Kunz CU, Ose D, Szecsenyi J, Peters-Klimm F. Patterns of multimorbidity in primary care patients at high risk of future hospitalization. Popul Health Manag. 2012;15:119–24. doi: 10.1089/pop.2011.0026. [DOI] [PubMed] [Google Scholar]
- 13.Edlund MJ, Martin BC, Fan MY, Braden JB, Devries A, Sullivan MD. An analysis of heavy utilizers of opioids for chronic noncancer pain in the TROUP study. J Pain Symptom Manage. 2010;40:279–89. doi: 10.1016/j.jpainsymman.2010.01.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Nuckols TK, Anderson L, Popescu I, et al. Opioid prescribing: A systematic review and critical appraisal of guidelines for chronic pain. Ann Intern Med. 2014;160:38–47. doi: 10.7326/0003-4819-160-1-201401070-00732. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Noble M, Treadwell JR, Tregear SJ, et al. Long-term opioid management for chronic noncancer pain. Cochrane Database Syst Rev. 2010:CD006605. doi: 10.1002/14651858.CD006605.pub2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Foley KM. The treatment of cancer pain. N Engl J Med. 1985;313:84–95. doi: 10.1056/NEJM198507113130205. [DOI] [PubMed] [Google Scholar]
- 17.Vissers KC, Besse K, Hans G, Devulder J, Morlion B. Opioid rotation in the management of chronic pain: Where is the evidence? Pain Pract. 2010;10:85–93. doi: 10.1111/j.1533-2500.2009.00335.x. [DOI] [PubMed] [Google Scholar]
- 18.Hallenbeck J. Palliative Care Perspectives. In: Oxford University Press. 2003:36–74. [Google Scholar]
- 19.Agency Medical Director's Group [April 7, 2014];Web-based opioid dose calculator. Available at http://agencymeddirectors.wa.gov/mobile.html.
- 20.Agency for Healthcare Research and Quality [December 12, 2014];Hospital inpatient utilization related to opioid overuse among adults, 1993-2012. Available at: www.hcupus.ahrq.gov/reports/statbriefs/sb177-Hospitalizations-for-Opioid-Overuse.pdf. [PubMed]
- 21.National Center for Health Statistics [December 12, 2014];Hospitalization for congestive heart failure: United States, 2000-2010. Available at: http://www.cdc.gov/nchs/data/databriefs/db108.htm#trends.
- 22.Edlund MJ, Austen MA, Sullivan MD, et al. Patterns of opioid use for chronic non-cancer pain in the veterans health administration from 2009 to 2011. Pain. 2014;155:2337–43. doi: 10.1016/j.pain.2014.08.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Mosher HJ, Jiang L, Vaughan Sarrazin MS, Cram P, Kaboli PJ, Vander Weg MW. Prevalence and characteristics of hospitalized adults on chronic opioid therapy. J Hosp Med. 2014;9:82–7. doi: 10.1002/jhm.2113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Kulshrestha S, Barrantes F, Samaniego M, Luan FL. Chronic opioid analgesic usage post-kidney transplantation and clinical outcomes. Clin Transplant. 2014;28:1041–6. doi: 10.1111/ctr.12414. [DOI] [PubMed] [Google Scholar]
- 25.Cahana A, Dansie EJ, Theodore BR, Wilson HD, Turk DC. Redesigning delivery of opioids to optimize pain management, improve outcomes, and contain costs. Pain Med. 2013;14:36–42. doi: 10.1111/pme.12013. [DOI] [PubMed] [Google Scholar]
- 26.Hyer LA, Walid MS, Brooks AM, Darmohray DM, Robinson JS., Jr. Interaction of age and opioid dependence on length of hospital stay for spine surgery patients. Psychol Rep. 2009;105:361–4. doi: 10.2466/PR0.105.2.361-364. [DOI] [PubMed] [Google Scholar]
- 27.Fan VS, Ramsey SD, Giardino ND, et al. Sex, depression, and risk of hospitalization and mortality in chronic obstructive pulmonary disease. Arch Intern Med. 2007;167:2345–53. doi: 10.1001/archinte.167.21.2345. [DOI] [PubMed] [Google Scholar]
- 28.Finkle WD, Der JS, Greenland S, et al. Risk of fractures requiring hospitalization after an initial prescription for zolpidem, alprazolam, lorazepam, or diazepam in older adults. J Am Geriatr Soc. 2011;59:1883–90. doi: 10.1111/j.1532-5415.2011.03591.x. [DOI] [PubMed] [Google Scholar]
- 29.Pizzi LT, Toner R, Foley K, et al. Relationship between potential opioid-related adverse effects and hospital length of stay in patients receiving opioids after orthopedic surgery. Pharmacotherapy. 2012;32:502–14. doi: 10.1002/j.1875-9114.2012.01101.x. [DOI] [PubMed] [Google Scholar]
- 30.Jena AB, Goldman D, Weaver L, Karaca-Mandic P. Opioid prescribing by multiple providers in Medicare: Retrospective observational study of insurance claims. BMJ. 2014;348:g1393. doi: 10.1136/bmj.g1393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Vogt MT, Kwoh CK, Cope DK, Osial TA, Culyba M, Starz TW. Analgesic usage for low back pain: Impact on health care costs and service use. Spine (Phila Pa 1976) 2005;30:1075–81. doi: 10.1097/01.brs.0000160843.77091.07. [DOI] [PubMed] [Google Scholar]
- 32.Strassels SA. Economic burden of prescription opioid misuse and abuse. J Manag Care Pharm. 2009;15:556–62. doi: 10.18553/jmcp.2009.15.7.556. [DOI] [PMC free article] [PubMed] [Google Scholar]