Self-Reported Prescription Opioid Use Among Participants with Chronic Spinal Cord Injury

Abstract

Background:

Individuals with spinal cord injuries (SCI) experience high rates of prescription opioid use, yet there is limited data on frequency of opioid use and specific medications being taken.

Objectives:

To examine the frequency of self-reported prescription opioid use among participants with SCI and the relationship with demographic, injury, and socioeconomic characteristics.

Methods:

A cohort study of 918 adults with SCI of at least 1-year duration completed a self-report assessment (SRA) that indicated frequency of specific prescription opioid use based on the National Survey on Drug Use and Health (NSDUH).

Results:

Forty-seven percent of the participants used at least one prescription opioid over the last year; the most frequently used was hydrocodone (22.1%). Nearly 30% used a minimum of one opioid at least weekly. Lower odds of use of at least one opioid over the past year was observed for Veterans (odds ratio [OR] = 0.60, 95% CI = 0.38, 0.96) and those with a bachelor's degree or higher (OR = 0.63, 95% CI = 0.44, 0.91). When restricting the analysis to use of at least one substance daily or weekly, lower odds of use was observed for those with a bachelor's degree or higher and those with income ranging from $25,000 to $75,000+. None of the demographic or SCI variables were significantly related to prescription opioid use.

Conclusion:

Despite the widely established risks, prescription opioids were used daily or weekly by more than 28% of the participants. Usage was only related to Veteran status and socioeconomic status indicators, which were protective of use. Alternative treatments are needed for those with the heaviest, most regular usage.

Introduction

The US opioid crisis and overdose epidemic is a public health emergency.¹ Since the 1990s, high rates of prescription opioid use and misuse, as well as illicit opioid use, have contributed to a significant number of nonfatal overdoses and opioid-related deaths. Despite reductions in opioid dispensing between 2012 and 2020, the overall opioid dispensing rate in 2020 remained high, at 43.3 prescriptions per 100 people.² Based on data from the 2021 National Survey on Drug Use and Health (NSDUH), 27% of Americans 18 and older reported past-year use of any opioids and 3.4% reported past-year misuse of opioids, including prescription and illicit use.³ Since 2019, there have been significant increases in both suspected nonfatal opioid overdoses⁴ and opioid-related drug overdose deaths.⁵ Between 2019 and 2020, there was a 31% increase in overdose deaths; of the nearly 92,000 drug overdose deaths in 2020, 75% were attributable to opioids. More specifically, prescription opioids were involved in nearly 24% of those deaths, which was a 16% increase in prescription opioid-related deaths since 2019.⁶ Drug overdoses involving any opioids continued to rise in 2021.⁵

Recent studies have found that compared to the US general population, individuals with spinal cord injury (SCI) experience higher rates of prescription opioid use, longer durations of opioid coverage, and higher opioid dosages.^7,8 Although they are not recommended as a first-line therapy, opioids are often prescribed for pain, a prevalent secondary health condition after SCI.^9,10 Specifically among Veterans with SCI, opioids were identified as the most frequently filled class of drugs, with upwards of 70% of the population being prescribed opioids for pain.^11,12 Findings suggest that high-risk use, including chronic use, high dosages, and concurrent use of opioids and benzodiazepines, is of concern among those with SCI and may increase the risk of adverse outcomes including overdose and death.^7,8,13,14

The potential dangers of opioid use after SCI cannot be overstated. For instance, a study of cause-specific mortality among 690 deaths after SCI identified 24 unintentional deaths due to drug poisoning; 11 of those were related to opioids.¹⁵ Despite the obvious risks, there are limited data on the self-reported frequency of opioid use, including the specific medications these individuals are taking. Our purpose was to examine the self-reported frequency of multiple types of prescription opioids and to identify the odds of their usage based on demographic, SCI, and socioeconomic characteristics.

Methods

Participants and data sources

We obtained Medical University of South Carolina institutional review board (IRB) approval prior to implementing the study. All individuals participated in earlier follow-up studies of persons with SCI. The eligibility criteria included traumatic SCI, noncomplete recovery, adult (>18 years old), and minimum of 1 year since the onset of SCI. Participants were initially identified from two cohorts of a regional specialty hospital in the southeastern United States; one first enrolled in 1997-1998 and the other enrolled in 2007-2010. An additional cohort was identified from a state-based surveillance system, also in the southeastern United States. The initial pool of 1856 cases was reduced by mortality (n = 449) to 1407. Of these, there were 918 respondents (65.2%).

Data collection procedures

Potential participants were sent cover letters to explain the study and let them know that materials would be forthcoming. Each participant was assigned a unique ID number. The letter contained the ID number and a link to the online REDCap self-report assessment (SRA) that allowed participants to complete the materials virtually. A paper form of the SRA was subsequently mailed 4 to 6 weeks later for all individuals who did not complete the materials in REDCap. A second mailing was used to promote response, followed by a phone call from the study coordinator. For individuals who agreed to participate but had discarded or misplaced their SRAs, another set of materials was sent by mail, followed by an additional phone call. Return of completed assessments implied consent, and a waiver of signed consent was obtained from the IRB. Participants were offered $50 in remuneration to compensate them for their participation.

Measures

Participants completed an SRA with items on demographic characteristics (age, race-ethnicity, sex), SCI characteristics (injury severity, years since SCI onset), socioeconomic indicators that included education and income, and 11 opioid use items from the NSDUH.¹⁶ Age was broken down into the following categories: 48 or less, 49-59, 60-69, and 70 or higher. Race/ethnicity was grouped as follows: non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic other. Neurologic level was broken down into four categories, the first of which was all ambulatory; the other three categories were broken down according to neurologic level as follows: high cervical (C1–C4), low cervical (C5– C8), and noncervical. Years since injury was broken down into the following categories: 1-9 years, 10-19 years, and 20+ years. Years of education was broken down into three levels: high school diploma/GED or less, trade school/associate's degree, and bachelor's degree or higher. Family income was broken down into three levels: <$25,000, $25,000-75,000, and >$75,000.

For the opioid use items, participants were asked whether they used each specific substance within the past 12 months and the frequency of use (never, once or twice, monthly, weekly, or daily/ almost daily). Participants were asked specifically about their use of prescription pain relievers, because they were more likely to understand the term “pain relievers” than “opioids,” because “pain relievers” indicates the purpose for which the drugs are likely to be taken. They were asked about their use of (a) hydrocodone (Vicodin, Lortab, Norco, Zohydro ER); (b) oxycodone (OxyContin, Percocet, Percodan, Roxicodone); (c) tramadol (Ultram, Ultram ER, Ultracet); (d) codeine (Tylenol with codeine 3 or 4); (e) morphine (Avinza, Kadian, MS Contin); (f) fentanyl (Duragesic, Fentora); (g) buprenorphine (Suboxone); (h) oxymorphone (Opana, Opana ER); (i) meperidine (Demerol); (j) hydromorphone (Dilaudid, Exalgo); or (k) other.

Data analysis

STATA Version 16.1 (STATA Corp., College Station, TX) was used for all statistical analyses. First, we examined the frequencies of any opioid use (yes/no) and weekly/daily opioid use (yes/ no). Nonresponse to individual items was treated as “no.” We then estimated multivariate models of binary indicators of any (yes/no) opioid use and weekly/daily (yes/no) opioid use, using logit model estimation methods. We included age, years since injury, SCI injury severity, race/ethnicity, military Veteran status, education, and household income as predictors. Odds ratios (OR) as well as 95% confidence intervals (95% CI) are reported.

Results

Participant characteristics

The majority of the participants were male (70.1%); 69.7% were non-Hispanic White, 25% non-Hispanic Black, 2% Hispanic, and 3.4% other. The average age of the participants was 57.5 years, with an average of 24.4 years having passed since SCI onset. Ambulatory participants comprised 43.6% of the participants. Of the remaining, 7% had C1–C4 injury, 20% had C5–C8 injury, and 29.4% were nonambulatory. Veterans comprised 12.3% of the cohort.

Frequency of opioid use

When considering any opioid use over the last 12 months, 47.1% of the participants used at least one opioid, 17.6% used two or more, and 5.6% used three or more opioids (Table 1). Hydrocodone was the most commonly used opioid (22.1%), followed by oxycodone (19.5%) and tramadol (15.6%; Table 2). The remaining substances were all reported by less than 10% of the participants. When considering self-reported weekly or daily use, 28.8% used at least one opioid, 6.9% used two or more, and less than 1% used three or more opioids. Oxycodone had the highest daily/weekly use (11.6%), followed by hydrocodone (9.3%).

Table 1.

Frequency of any opioid use over the last 12 months

Frequency	n (%)
No opioid use	486 (52.9)
At least one opioid	271 (29.5)
Two opioids	110 (12.0)
Three or more opioids	51 (5.6)

Table 2.

Past 12-month use: Most commonly reported prescription pain relievers

Medication	Any use n (%)
Hydrocodone	188 (22.1)
Oxycodone	167 (19.5)
Tramadol	132 (15.6)
Codeine	75 (8.9)
Morphine	49 (5.9)
Fentanyl	21 (2.5)
Hydromorphone	19 (2.3)
Buprenorphine	14 (1.7)
Methadone	13 (1.6)
Oxymorphone	5 (0.6)
Meperidine	5 (0.6)

The demographic, SCI, and socioeconomic variables accounted for only .0086 of the variation in annual opioid use (i.e., having used at least one opioid over the past year). Veterans had lower odds of use compared to non-Veterans (OR = 0.60, 95% CI = 0.38, 0.96, p = .032), and those with a bachelor's degree or higher reported lower odds of opioid use compared with those with a high school degree or less (OR = 0.63, 95% CI = 0.44, 0.91, p = .015; Table 3). Demographic, SCI, and family income were unrelated to opioid use.

Table 3.

The odds of any use of at least one prescription opioid within the past 12 months as a function of demographic, SCI, and socioeconomic characteristics

Variable	Odds ratio	95% CI	p
Age, years (ref: 48 or less)
49-59	1.00	.69, 1.47	.991
60-69	.914	.61, 1.36	.658
70 or higher	1.08	.69, 1.69	.737
Years postinjury (ref: 1-9 years)
10-19	.21	.03, 1.78	.153
20+	.23	.03, 1.89	.170
Race/ethnicity (ref: non-Hispanic White)
Non-Hispanic Black	.91	.64, 1.30	.609
Non-Hispanic other	.97	.46, 2.03	.930
Hispanic	.90	.33, 2.45	.834
Injury level (ref: C1–C4)
C5–C8, nonambulatory	.78	.42, 1.46	.441
Noncervical, nonambulatory	1.07	.59, 1.93	.827
Ambulatory	1.05	.59, 1.88	.864
Veteran	.60	.38, .96	.032
Education (ref: high school diploma/GED or less)
Associate's/2-year degree	1.08	.76, 1.54	.675
Bachelor's or higher	.63	.44, .91	.015
Household income (ref: <$25,000)
$25,000-$75,000	.80	.56, 1.13	.204
>$75,000	.67	.44, 1.02	.061

When restricting the analysis to use of at least one opioid weekly or daily (Table 4), the covariates accounted for .0387 in variation of weekly or daily use of at least one opioid. Having a bachelor's degree or higher was again associated with lower odds of opioid use (OR = 0.55, 95% CI = 0.36, 0.85). In addition, lower odds of weekly/daily opioid use was associated with having household income of $25,000-75,000 (OR = 0.54, 95% CI = 0.36, 0.80) and >$75,000 (OR = 0.59, 95% CI = 0.36, 0.95). None of the demographic or SCI variables, nor Veteran status, were associated with the odds of at least weekly/daily use.

Table 4.

The odds of at least daily or weekly use of at least one prescription opioid as a function of demographic, SCI, and socioeconomic characteristics

Variable	Odds ratio	95% CI	p
Age, years (ref: 48 or less)
49-59	1.31	.86, 2.00	.203
60-69	1.21	.77, 1.90	.417
70 or higher	1.35	.81, 2.23	.248
Years postinjury (ref: 1-9 years)
10-19	.54	.12, 2.56	.442
20+	.53	.11, 2.47	.418
Race/ethnicity (ref: non-Hispanic White)
Non-Hispanic Black	.76	.51, 1.13	.177
Non-Hispanic other	1.01	.42, 2.40	.990
Hispanic	.62	.19, 2.05	.432
Injury level (ref: C1–C4)
C5–C8, nonambulatory	.84	.43, 1.66	.624
Noncervical, nonambulatory	1.21	.64, 2.31	.556
Ambulatory	.92	.49, 1.74	.801
Veteran	.72	.43, 1.20	.202
Education (ref: high school diploma/GED or less)
Associate's/2-year degree	1.09	.75, 1.59	.653
Bachelor's or higher	.55	.36, .85	.007
Household income (ref: <$25,000)
$25,000-$75,000	.54	.36, .80	.002
>$75,000	.59	.36, .95	.031

Discussion

This study was designed to address a gap in the literature related to prescription opioid use among individuals with SCI by identifying the frequency of use of different types of opioids and their relationship with demographic, SCI, and socioeconomic characteristics. The statistical modeling of variables related to opioid use further suggest that there are relatively few differences as a function of individual characteristics. We found that Veteran status and having a bachelor's degree or higher were significantly associated with the odds of using at least one prescription in the past 12 months, and higher education and income were significantly predictive of use on a daily or weekly basis.

Whereas the study provided important information on frequency of use of multiple types of opioids, the predictive models accounted for a very small portion of variance, with no significant relationships between opioid use and SCI or demographic variables (only Veteran status and socioeconomic indicators were significant, being protective of opioid use). This is encouraging as we observed no racial/ethnic-, sex-, or disability-related disparities in risk. The absence of relationships between demographic, SCI, and, to a lesser extent, socioeconomic factors suggests that opioid use is likely related to other factors that hopefully are modifiable and will represent targets for intervention. Unfortunately, because of the absence of research on opioids and SCI, there is an absence of research to guide interventions on modifiable factors.

This study adds to the knowledge generated by previous studies of opioid use among those with SCI by focusing on self-reported use patterns, allowing researchers and clinicians to view opioid use through a different lens. While findings from claims data and prescription drug monitoring programs show the rates of fills, it is not clear if the medications are taken as prescribed once filled. By assessing self-reported use, we are able to better determine prevalence of use and what medications are actually being used. We found a high rate of past year opioid use among our SCI participants (47.1%), which falls within the range of reported rates (35%-64%),¹⁷ but it is much greater compared to rates of use reported in the general population from the NSDUH (27%).¹⁶ In addition to providing self-reported rates of use, we were able to identify the specific medications used and the total number of medications used in the past year, findings that have implications for better understanding risky and concomitant medication use.

Our findings add important information regarding opioid use among Veterans with SCI, a population that has previously been shown to have high rates of prescription opioid fills^11,12 and for which opioid safety initiatives have been implemented to address opioid overuse. In 2013, the Department of Veterans Affairs (VA) implemented a systemwide opioid safety initiative; in 2017, the VA and Department of Defense published clinical practice guidelines for reducing the use of opioids in the management of chronic pain, which were updated in 2022.¹⁸ Through an interdisciplinary approach, the VA has successfully reduced the number of patients receiving opioid prescriptions, the number of patients on long-term opioids and high dosages, and the number of patients receiving concurrent benzodiazepine and opioid prescriptions.^19-22 Our findings suggest that these initiatives have translated into a population benefit, as the Veterans in our cohort had a lower odds of opioid use compared to non-Veterans.

Regarding education and income, previous studies of the general population have found that lower levels of income and education are associated with opioid use and misuse.²³ However, there has been limited focus on social determinants related to opioid use behaviors among those with SCI. In one study of adults with nontraumatic SCI, higher income was found to be protective against receiving opioids after discharge.²⁴ Lower income has also been identified as a risk factor for chronic opioid use among those with traumatic SCI.¹³ Although we did not find the odds of past year opioid use to be associated with income, we did find that the odds of past year weekly or daily opioid use were lower for those with income greater than $25,000. Additionally, having attained a higher level of education (bachelor's degree or higher) was associated with lower odds of weekly or daily use.

Implications for rehabilitation professionals

The majority of contact between rehabilitation professionals and people with SCI occurs shortly after injury onset. After that, some individuals may stay in contact, whereas others are absorbed into communities with lesser access to rehabilitation specialties, where a portion of the responsibility shifts to public health. Therefore, it is essential that during initial or early rehabilitation, individuals are informed of the potential risks of opioid use and taught alternative techniques for pain management. Clinicians providing care to those with longstanding SCI should weigh the risks and benefits of opioid therapy and consider other factors related to high-risk use, including high dosages, concurrent medication use, and chronic use.

Implications for public health

Because the cohort used in this study was comprised of long-term survivors, the findings are particularly relevant to public health officials for whom it is incumbent to monitor health behaviors, morbidity, and mortality in the population. The current findings raise concerns for people with SCI who comprise a special population, likely part of a larger pattern for people with mobility impairments. Public health officials need to find ways of enacting policies that monitor opioid use within the context of population health and look for strategies to limit its occurrence to only necessary circumstances while identifying its impact on health outcomes.

Methodological considerations

There are several methodologic considerations when interpreting the findings. First, all data were self-report. The advantage of self-report is that it may focus on actual usage of prescription medications, whereas administrative and billing data only measure what has been prescribed, which can be affected by diversion (i.e., individuals selling a portion of the medications),²⁵ by whether individuals follow the directions for use, or by whether they do not use all of the medication. On the other hand, self-report data are susceptible to reporting errors due to recall, understanding of specific medications, or conscious reporting of sensitive behaviors. We chose self-report because it is necessary to use multiple methods (methodologic triangulation) to understand opioid use, including both administrative types of billing data and self-report of actual behaviors. Administrative billing data have been used elsewhere.^7,15 Therefore, the current study adds one type of valuable information to our understanding of opioid use. Second, a large portion of individuals reported use of “other.” The specific nature of these medications is unknown. We did not include them in the annual analysis because of uncertainty as to whether the medications they were referring to were indeed opioids. Therefore, to the extent that use of the other category represented valid opioid use, our percentages are somewhat conservative. Third, we treated items that were left blank as indicating nonuse. We believe this is appropriate because the vast majority of missing responses were instances where the individual indicated use of one or more substances and then left other items blank. In instances where leaving items blank was indeed nonresponse, rather than a passive indicator of nonuse, our usage estimates may be conservative. Fourth, in terms of sampling, we used a combination of participants identified from statewide population-based surveillance, which captures all civilian cases within a given region and those identified through a model system of care. This is a distinctive strength of the study. However, all participants were from the southeastern United States and may not be fully representative of other geographic regions within the country or internationally. Fifth, although the current study helps us understand the pattern of prescription opioid use among those with SCI, it does not offer clues as to how to change opioid use patterns or the relationship of opioid use to outcomes. Lastly, our cohort was comprised of participants with chronic SCI who averaged 57.5 years of age and 24.4 years since SCI onset. Therefore, the findings primarily apply to those who are relatively long-term survivors, rather than newly injured.

Future research

We have only begun to understand the extent of opioid use among people with SCI and the types of medications that are most frequently used. We need to identify how opioid usage relates to other types of prescription medication use, such as benzodiazepines, which if used concurrently would constitute high-risk use. We also need to identify other risk and protective behaviors and environmental factors that predict opioid use. The current study was focused on describing the most general usage patterns rather than the more detailed dynamics of the relationships with psychological and other behavioral factors. Identifying these dynamics will be essential to identifying and targeting modifiable factors in relation to opioid use and opioid-related problems. Furthermore, it is critical to understand the relationship of opioid use, and specific types of opioid use, with prominent outcomes that include participation, health, and quality of life. It is only through systematic research that we will understand the full scope of risk related to opioids.

Conclusion

Prescription opioids were used weekly or daily by more than 28% of the participants, with nearly 50% reporting at least one use annually. Usage was not related to demographic or SCI characteristics but only to Veteran status and socioeconomic status indicators, all of which were protective of use. Given the high risk of opioid use and the emphasis upon reducing prescription opioids, alternative treatments are needed for those with the heaviest, most regular usage.

Funding Statement

Financial Support The contents of this publication were developed under grants from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), 90DPHF0009. NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this publication do not necessarily represent the policy of NIDILRR, ADL, or HHS, and you should not assume endorsement by the funding agency or Federal Government.