Results 9% of opioid users also used a benzodiazepine in 2001, increasing to 17% in 2013 (80% relative increase). This increase was driven mainly by increases among intermittent, as opposed to chronic, opioid users. Compared with opioid users who did not use benzodiazepines, concurrent use of both drugs was associated with an increased risk of an emergency room visit or inpatient admission for opioid overdose (adjusted odds ratio 2.14, 95% confidence interval 2.05 to 2.24; P<0.001) among all opioid users. The adjusted odds ratio for an emergency room visit or inpatient admission for opioid overdose was 1.42 (1.33 to 1.51; P<0.001) for intermittent opioid users and 1.81 (1.67 to 1.96; P<0.001) chronic opioid users. If this association is causal, elimination of concurrent benzodiazepine/opioid use could reduce the risk of emergency room visits related to opioid use and inpatient admissions for opioid overdose by an estimated 15% (95% confidence interval 14 to 16).

As prescribing behaviors are likely to vary nationally and across clinical settings, 28 29 however, the applicability of these findings to the broader population (including to veterans, most of whom do not access VHA care) is unclear. For example, compared with the general population, veterans in the US have a higher prevalence of substance misuse and mental health disorders. 30 31 32 We focused on concurrent benzodiazepine/opioid use in a privately insured population broadly representative of the entire US, in whom concurrent use was defined as one day of overlap in the time periods covered by each prescription. We have built on previous work by focusing on trends in concurrent benzodiazepine/opioid use over time and their effects on population health, which has not been fully characterized. Using a large dataset of administrative health claims data, we explored trends in concurrent use in 2001-13. In addition, we examined the degree to which patients using these two prescribed drugs have an increased risk of an emergency room visit or inpatient admission for opioid overdose. Finally, we examined the degree to which reducing concurrent use could reduce the risk of emergency room visits and inpatient admissions for opioid overdose at the population level.

A recent study examined the incidence of opioid and benzodiazepine use among the subset of the veteran population who receives care from the Veterans Health Administration (VHA). Nearly 30% of VHA patients who were prescribed opioids also received a concurrent prescription for benzodiazepines, defined as having at least one day’s overlap between a benzodiazepine and opioid prescription in a given calendar year. 24 25 Moreover, this study found that co-prescribing was associated with a significantly higher risk of death than with the use of opioids alone. Similar results were found in studies examining opioid prescriptions in North Carolina 26 and in Ontario, Canada. 27

Nearly 30% of fatal “opioid” overdoses also involve benzodiazepines, which are often used concurrently with opioids, 16 17 18 raising the possibility that some of the increase in opioid related deaths could be caused by increases in concurrent benzodiazepine/opioid use over time. Although benzodiazepines have received less public safety attention than opioids, the combination of the two drugs is dangerous because benzodiazepines potentiate the respiratory depressant effects of opioids. 19 Indeed, the US Food and Drug Administration (FDA) recently released a “black box” caution, warning patients and providers about the potential risks of combined use. Understanding the degree to which concurrent benzodiazepine/opioid use has increased over time, as well as the magnitude of its potential adverse effects, could have important implications for policy and clinical practice. These concerns are particularly salient in the US, but there is also some evidence of high rates of concurrent use internationally. For example, one study found that 47% of patients in methadone treatment programs in Spain also used benzodiazepines, 20 while a another study reported that nearly 52% of Swiss patients in methadone treatment programs were “regular” benzodiazepine users. 21 Studies have also found high rates of benzodiazepine use among heroin users in Australia. 22 23

In the US, the increased use of prescription opioids and the resulting potential for addiction and overdose impose substantial public burden of morbidity, mortality, and economic costs. 1 2 Opioid prescriptions have increased sharply—nearly threefold—over the past fifteen years, 3 with a concurrent increase in opioid related overdoses and deaths. 3 4 5 As a result, policymakers and researchers have expended considerable effort towards finding ways to reduce the misuse of, and overdose from, opioids. 6 7 8 9 10 11 12 13 14 15

Methods

Data We obtained a sample of administrative health claims provided by Marketscan (Truven Health Analytics, Ann Arbor, MI). Marketscan provides patient level data on use and expenditures for the care of patients enrolled in private insurance plans through a participating employer, health plan, or government organization. The database has grown from six million beneficiaries to comprise over 35 million beneficiaries today. Compared with the general US population, the Marketscan population includes more women, is more likely to come from the southern areas of the US, and is less likely to be drawn from the western areas of the US.33 The data are frequently used in analyses of healthcare use and spending.34353637 Our data include all claims from 1 January 2001 to 31 December 2013, inclusive. As we used de-identified patient data, institutional review board approval was not required. The information on inpatient and outpatient data claims provided details from specific encounters, including diagnosis codes (ICD-9 (international classification of diseases, ninth edition)), procedure codes (current procedural terminology, CPT), and date of service provision. For the pharmacy claims data, the information provided includes fill date, quantity supplied, and number of days supplied. The data also provide the National Drug Code, which can be linked to Red Book data (Truven Health Analytics, Ann Arbor, MI) to obtain the generic name and dose of the prescribed drug.

Sample Our initial sample consisted of the 595 410 patients who were continuously enrolled in a plan with medical and pharmaceutical benefits from 1 January 2001 to 31 December 2013. We restricted our analysis to patients who were continuously enrolled during the study period because, as noted above, the set of employers and health plans contributing data to Marketscan has markedly increased over time, leading to a large increase in the number of people in the database. Our approach thus reduces the risk of confounding that might occur because of changes over time in the underlying population reporting data to Marketscan. From this sample, we identified and excluded patients with a history of cancer or those who received a diagnosis of cancer during the study period (n=28 780) as well as those aged under 18 or over 64 when they first entered the study (n=142 789), giving a sample of 423 841 patients. Our final sample was the subset of patients (315 428) who filled at least one prescription for an opioid during the study period. A flow diagram (fig A) describing the construction of our sample is in the appendix.

Outcomes Our primary outcome was an emergency room visit or inpatient admission for opioid overdose within a given calendar year. Using methods described elsewhere,38 we defined opioid overdose to be an admission or visit with ICD-9 codes indicating either opioid related poisoning or a potential opioid related adverse event (such as respiratory depression) and an ICD-9 code corresponding to opioid overdose. For each opioid prescription, we defined a time interval starting the day the prescription was filled and lasting the number of days supplied in the prescription. We counted visits only if they occurred during this time interval or within seven days after the end of this interval. For example, if a patient received an opioid prescription on 1 January 2007 with 10 days’ supply, we counted only visits that occurred between 1 January 2007 and 17 January 2007. In our sensitivity analyses we considered alternative definitions, such as visits occurring within 30 days of the time interval previously described.

Variables Our key independent variable of interest was whether an opioid user also used a benzodiazepine concurrently within a given calendar year. First, we identified opioid use by isolating all prescriptions for outpatient opioids (table A in appendix), excluding prescriptions containing hydrocodone in a cough/cold formulation. We then isolated all prescriptions for a benzodiazepine (table B in appendix) and directly examined the degree of temporal overlap between prescriptions among individuals who filled a prescription for both classes of drugs. Specifically, for each opioid prescription, we defined an interval in which the prescription took effect as the interval starting on the day the prescription was filled and lasting up to the number of day’s supply provided in the prescription. We defined a similar interval for a benzodiazepine prescription and quantified the total number of opioid prescription days that overlapped with a benzodiazepine prescription days. For example, suppose a given patient filled an opioid prescription and received a 30 day supply on 1 January 2001. If the same individual filled a benzodiazepine prescription on 20 January 2001 with 30 days’ supply, then 11 out of the 30 days of the opioid prescription overlapped with a benzodiazepine prescription. For our baseline analyses, we defined concurrent use as having at least one day of overlap in a given calendar year,2439 in line with previous studies. We also considered alternative definitions of concurrent opioid/baseline in our sensitivity analyses. Our analysis included several controls for patients’ demographics and health. Age and sex were directly obtained from the claims data. ICD-9 diagnosis codes were used to control for comorbidities including diabetes mellitus and congestive heart failure (table 1⇓ provides a full list of comorbidities).40 For each comorbidity, we identified the earliest year with at least two claims containing the associated ICD-9 codes (table B in appendix) and defined the patient as having a history of the given comorbidity from that year onwards. Finally, we also controlled for total healthcare spending in the time period before the first opioid prescription in a given year. To do so, we isolated all pharmacy, inpatient, and outpatient claims submitted before the earliest opioid prescription in a given year. We then summed the spending across all these claims and divided by the number of calendar days in the interval between 1 January of the given year and the date of the earliest opioid prescription. Table 1 Characteristics of study population with any opioid use at start of study period (2001) according to concurrent filled prescription for benzodiazepine. Figures are numbers (percentage; 95% CI) of patients meeting criteria (unless stated otherwise) View this table:

Analyses We first calculated the annual percentage of opioid users with concurrent benzodiazepine use. We stratified our analysis by intermittent and chronic opioid users. Following previous work,10 chronic users were defined as patients who filled more than 10 prescriptions or had more than 120 days’ supply in a given year, with the remaining opioid users being defined as intermittent users. Because our study sample consisted of patients who were continuously enrolled during the study period, the average age of our population increased by one year annually. Therefore, we calculated age adjusted estimates using methods described in the appendix. We then used multivariate logistic regression to estimate the association between concurrent benzodiazepine/opioid use and opioid overdose among opioid users. The dependent variable in this regression was an indicator variable that equaled 1 if the patient had at least one emergency room visit or admission for opioid overdose (using the methods described above) in the given calendar year and 0 otherwise. Our independent variable of interest was an indicator variable that equaled 1 if the opioid user met the criteria for concurrent benzodiazepine use in the given year and 0 otherwise. We also included controls for age, year, and the set of additional variables in table 1⇑. Finally, we calculated the population attributable fraction (PAF) of concurrent benzodiazepine/opioid use to the risk of opioid overdose. This fraction represents the relative risk reduction for a given event at the population level under a counterfactual scenario for a specific risk factor. For example, the population attributable fraction has been used to describe the degree to which low birth weight would be reduced if maternal smoking could be eliminated entirely.41 In our case, we calculated the population level risk reduction that would occur if concurrent benzodiazepine/opioid use could be eliminated entirely. These estimates were calculated by using the results from the logistic analyses described above, following methods described elsewhere.4243 Because the unit of observation in our data is a person year, patients will contribute multiple observations if they used opioids in more than one calendar year. We therefore adjusted our standard errors for clustering at the patient level.44 All analyses were performed with Stata 14.0 (College Station, TX).

Sensitivity analyses We conducted several sets of sensitivity analyses. First, in our baseline analyses we defined concurrent benzodiazepine/opioid overdose as requiring at least one day of overlap; we considered an alternative (stricter) definition that required 25% of the days’ supply of opioids to overlap with a benzodiazepine prescription. Similarly, our baseline analyses defined an opioid overdose as an emergency room visit or admission occurring during the time interval covered by an opioid prescription or within seven days after the end of the prescription; we considered alternative definitions that both loosened (allowing a visit to occur within 30 days after the time interval covered by an opioid prescription) and tightened (requiring the visit to occur exactly during the interval covered by an opioid prescription) this criterion. Second, a potential issue arises because our sample was constructed as a set of individuals who were continuously enrolled between 2001 and 2013. The advantage of this approach is that allows us to follow a uniform set of people over time. By contrast, other approaches—such as including all patients regardless of enrollment duration—would have the drawback of having to deal with a changing population over time as people enter and exit the sample. Restriction to people who did not leave the sample (because of death or loss of employment), however, could lead to bias because people who die (or lose employment) secondary to opioid use would probably have experienced a series of emergency room visits or inpatient admissions for opioid overdose before the actual event. To the degree that the concurrent benzodiazepine/opioid use increases the risk of these events (and of attrition), our approach will therefore underestimate the true effect of concurrent use (as our sample is limited to people who did experience these events, but not enough to result in death or loss of employment). To deal with this, we conducted a secondary analysis using a broader sample consisting of all people who were continuously enrolled for at least two years (n=3 810 747). Each individual remained in the sample until the study end date or until they exited the sample. Thus, this broader sample includes our original sample as well as patients who subsequently entered and exited the sample. Finally, one potential source of bias is that opioid users who concurrently used benzodiazepines could differ from those who did not. We performed a residual confounding analysis4546 to investigate the extent to which our results could be explained by other unobservable factors, such as differences in health status between the two groups. Specifically, we assumed the presence of an unmeasured binary confounder that was patient specific and independent of our measured confounders. We assumed that this confounder had a prevalence of 75% among our surgical sample and 0% among the non-surgical patients. The assumed difference in prevalence between surgical and non-surgical patients of this unmeasured confounder is much larger than the difference in prevalence for all the medical comorbidities we examined. Using methods described elsewhere, we then estimated the degree of confounding that would be necessary for this confounder to eliminate the estimated association between opioid overdose and concurrent benzodiazepine/opioid use.46