Ten insights on the US opioid crisis from claims data analysis

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The opioid crisis remains one of the United States’ most alarming and daunting public health problems. Combatting the crisis is far from easy. However, a rich source of data that could help inform discussions about ways to address opioid abuse—and improve pain management practices—is available: health insurers’ claims.

Here, we share ten insights we developed through claims analysis.1 These insights are intended to spark both dialogue and further investigations. They may raise more questions than they answer, but we believe that even identifying the most important questions to ask will be valuable for tackling the crisis.

First, however, we would like to acknowledge two limitations in our analysis. Because we used data sets from several state Medicaid programs, our insights cannot always be generalized to all payer contexts or all geographies. Also, while we believe that claims analysis alone can produce robust findings, we acknowledge that other data types—clinical and nonclinical—could increase precision. Nevertheless, the insights we developed and questions they triggered demonstrate the help that claims analytics can provide in identifying underlying issues and developing more rigorous initiatives to battle the crisis.

Insight 1

Opioid prescribing is widespread—it does not result primarily from outlier prescribers.

As the media often notes, a small set of prescribers has very high opioid prescribing rates. In our analysis, the top 1% of prescribers were responsible for only 5% of all opioid prescriptions and 21% of morphine-equivalent doses (MEDs).2 However, the next one-quarter of prescribers were responsible for 50% of the prescriptions and nearly 70% of all MEDs.

Question for discussion:

  • How can initiatives to improve opioid prescribing patterns engage many, if not most, prescribers?

Insight 2

Prescribing patterns vary significantly by geography, even among patients undergoing similar types of care.

The rate of opioid prescribing varies among regions and communities, even when measured within a single type of care. Exhibit 1 illustrates this in patients undergoing treatment for low back pain. We found, for example, that opioid dosing patterns (as measured by MEDs per day) varied by 75% or more across zip codes. Furthermore, there was little consistency in dosing patterns. Some zip codes had high opioid prescribing rates but total MED dosing close to median levels. Other zip codes exhibited the opposite pattern.

Opiod prescription rates and dosing patterns vary considerably from region to region

This heterogeneity held true for many other factors related to the opioid crisis. We spotted geographic variations in the frequency of doctor and pharmacy shopping, the rates of opioid prescribing by specialty and condition, and access to prevention and treatment resources.

Drilling deeper into these geographic differences, we used social network analysis to identify patterns of prescribers and pharmacies that appeared to be associated with an increased risk of opioid prescribing—and thus might be areas to prioritize for intervention. We found that in some communities, opioid prescription fills were concentrated in a few pharmacies, even though the providers “linked” to those pharmacies had average prescribing rates. We also observed the opposite: in other communities, a few prescribers stood out from the pack, but no subset of pharmacies had a disproportionate concentration of opioid prescription fills.

Question for discussion:

  • How can strategies to combat the opioid crisis best avoid a “one-size-fits-all” approach to program design, and instead tailor approaches and resourcing to match local needs and dynamics?
  • How can advanced analytics be used to define common patterns of the effect the opioid crisis is having on communities, which could then be used to help guide the design and execution of local responses?

Insight 3

Within a single region, prescribing patterns often vary significantly, even when providers are treating similar clinical problems or types of patients.

Many analyses have compared opioid prescribing rates among a large group of providers or within a single specialty. However, that approach does not take into account differences in the types of patients being treated. We believe that the best lens to use for comparison is an episode of care (all the treatment needed for a given condition over a defined length of time).3 The episode lens allows apples-to-apples comparisons on prescribing practices because it enables comparisons within similar clinical scenarios.

Using this approach, we found—not surprisingly—that the rate of opioid prescribing was much higher for many types of procedural care, such as orthopedic surgery, than for acute medical care (e.g., headache management or the nonsurgical treatment of low back pain). In fact, we found that for many procedures, opioids were prescribed, on average, in more than 70% of the episodes. But within the same type of episode (almost regardless of the episode type), prescribing rates varied greatly, with highest-quartile episodes associated with a 50% to 100% greater incidence of opioid prescribing than median episodes.

Even when we looked at specific episodes, we still found that opioid prescribing rates varied widely (Exhibit 2). For example, we learned that some providers never prescribe opioids for sprains; others always do. These patterns were consistent. Other types of acute medical care also had large variations in opioid prescribing rates (although at rates generally lower than those in procedural episodes).

Opiod prescription rates vary both across and within similar clinical scenarios

Question for discussion:

  • What would it take to drive greater agreement, both in theory and in practice, on how providers should treat pain for the same clinical conditions?
  • What are the most effective ways for provider associations and state and federal health agencies to develop and clarify guidelines and best practices in pain management? And what is needed to increase compliance with them?

Insight 4

Even within an individual provider’s clinical practice, opioid prescribing patterns may vary significantly, depending on the type of problem being treated.

In our analysis, a provider’s high opioid prescribing rate for one type of episode often failed to predict his or her prescribing rates for other episodes—even when the episodes were similar. For example, we compared prescribing rates among the providers who were “quarterbacks” for three orthopedic episodes: ankle sprains, knee sprains, and shoulder sprains. Almost none of the providers exceeded the median in opioid prescribing rates for all three of the episodes—but nearly 90% of them had at least one episode type with a rate above the median (Exhibit 3).

Prescribing patterns vary even within a provider's clinical practice, depending on the condition

These results suggest that even for the same provider, pain management practices with opioids are not consistent across episodes.

Question for discussion:

  • How might providers consider not only their opioid prescribing rate relative to their peers, but also how their pain management practices apply to specific episodes of care?
  • What are the most promising opportunities to engage most, if not all, providers—even those who typically appear to follow guidelines—and encourage them to consider whether they need to alter their prescribing patterns?

Insight 5

Patients with opioid use disorders are heterogeneous, but can be grouped into archetypes.

Cluster analyses indicate that patients with opioid use disorders can be grouped into several different archetypes (Exhibit 4). While the patients may have opioid use in common, other factors—including the concurrence of behavioral health and medical conditions, and socioeconomic factors—correlate strongly with different utilization patterns (e.g., emergency department (ED) and inpatient use, overall cost to the health system).

Cluster analysis makes it possible to group patients with opiod use disorders into addressable segments

Furthermore, treatment patterns appear to be influenced by these other factors—the archetypes differ greatly in their ranges and rates of methadone use (15% to 50%) and acute detoxification treatment (7% to 40%).

Question for discussion:

  • To what extent are the treatment programs that work for some patients likely to work for others?
  • What might be done to effectively categorize patients, stratify their risk, and match them to the most effective treatment protocol?

Insight 6

Providers frequently prescribe opioids to patients with known or potential risk factors for abuse.

A separate analysis found that 60% of the providers had prescribed opioids to patients with at least one of these features: having a non-opioid substance use disorder (SUD), being diagnosed with two or more behavioral health issues other than an SUD, filling opioid prescriptions from more than four providers in the past six months, or using more than four pharmacies to fill opioid prescriptions in the past six months.

In recent years, data-driven efforts have intensified to equip providers with more information about the patients to whom they are prescribing pain medications. But even though the use of electronic health records has increased, aggressive action is still needed to close the gap in transparency, knowledge, and practice.

Question for discussion:

  • What factors are contributing to opioid prescribing to patients with known or potential risk factors?
  • Do providers know about these risk factors and prescribe opioids anyway, or are they unaware of them? If they are unaware, is the problem difficulty in accessing patient data (e.g., via electronic medical records or a prescription drug monitoring program), or is it a failure to seek the information?

Insight 7

In one analysis, more than one-third of the patients had a known or potential risk factor for abuse.

Approximately 35% of the patients given opioid prescriptions in our analysis had features that put them at increased risk for opioid abuse (Exhibit 5). The features we found most often were the presence of a non-opioid SUD (17%) and the presence of two or more behavioral health diagnoses other than SUD (14%).

More than one-third of patients give opiod prescriptions may be at risk increased risk for abuse

Question for discussion:

  • For patients who receive opioids but have risk factors for abuse, what are the most effective care pathways and interventions to mitigate the risk?
  • How do these pathways and interventions differ, based on a patient’s specific risk profile?
Revisiting the access imperative

Revisiting the access imperative

Insight 8

Patients with concurrent prescriptions for an opioid and a behavioral health condition appear to have a 30% or greater likelihood of developing future opioid dependence.

We performed claims-based predictive modeling to identify which patients using opioids were most likely to be diagnosed with opioid dependence or an opioid use disorder within the next year. The model identified several factors associated with future opioid dependence (Exhibit 6). For example, each prescription filled for a behavioral health condition (assuming all else was equal) was associated with a 2.5% to 4.6% increase in the likelihood that a patient using opioids would develop dependence or abuse within the next year. (The comparison was with patients using opioids but not taking a behavioral health medication.) This finding suggests that a patient who took a behavioral health medication for one year could have a 30% or greater increase in the risk of developing future opioid dependence or abuse.

Predictive modeling can identify risk factors for opiod dependence

In our study, additional factors found to be predictive of future opioid dependence or abuse included the presence of a behavioral health diagnosis such as anxiety, bipolar disorder, or recent suicide attempt (regardless of whether a behavioral health medication had been prescribed) and certain demographic factors (e.g., age 35 to 44).

We agree with healthcare researchers and policymakers that the integration of additional data sources (e.g., from social media, the criminal justice system, or prescription drug monitoring) would enrich our predictive modeling. Nevertheless, our claims-based model alone produced promising results—a 66% capture rate and 76% overall accuracy.4 Predictive modeling is critical to better inform providers and enable them to improve their pain management practices.

Question for discussion:

  • To what extent do the correlations we found imply causality? In particular, what are the interactions between opioid use and behavioral health conditions?
  • How could (or should) a patient’s risk profile and predictive modeling influence practice patterns, medical and pharmacy policy, clinical guidelines, and care management protocols?

Insight 9

Most opioids are prescribed by providers other than the natural “quarterback” of a patient’s underlying complaint or condition.

Analysis of one episode (spinal fusion) showed that nearly 80% of the total MEDs used by patients were not prescribed by the surgeon who had performed the procedure and who was responsible for the immediate “patient journey” after the procedure (we refer to such a provider as the principal accountable provider, or PAP).5 Furthermore, there was no apparent relationship between the amount prescribed by the PAP and the total amount prescribed by all providers during the episode.

This finding makes clear that high-dose prescribers and multi-prescriber patterns are separate issues—and both are important to address. All providers should recognize that they are part of a care team for a patient’s pain management and should make efforts to be aware of what other providers are prescribing to their patients.

Question for discussion:

  • Should the provider accountable for most other aspects of a patient’s care pathway also be responsible for managing that person’s pain, including the appropriate use of opioids when indicated?
  • If so, what strategies and tools can best enable providers to influence others’ prescribing patterns, whether the other providers are in their own practice or are consultants or specialists their patients see throughout the course of care?

Insight 10

A small portion of opioid use originates in emergency departments.

In our study, opioid prescribing in the ED was both less frequent and contributed less to total MEDs than was prescribing by other providers in other care settings (e.g., primary care physicians or outpatient specialists). In our analysis of spinal fusion and low back pain episodes, EDs accounted for only 5% of the opioid prescriptions and 1.4% of total MEDs. Among the low back pain episodes, 10% of the prescriptions and 1.2% of total MEDs came from the ED.

Question for discussion:

  • How can EDs improve their pain management practices, even though the proportion of opioid prescribing coming from EDs is low?
  • How can comprehensive opioid strategies address both ED and non-ED prescribing patterns?

We believe these ten insights can improve collective understanding of the opioid crisis in the United States and have identified important questions to answer. The insights concentrate on providers and patients in the healthcare delivery system. Additional work is needed, especially a widening of the aperture to focus on contributing factors beyond providers and patients. Options that should be included are investigation of non-claims data sources, integration of data sources, and a mix of descriptive, predictive, and prescriptive analytics to improve the design of opioid initiatives.

Nevertheless, this analysis represents a robust starting point. Despite the complexity of the crisis, our growing ability to use data and analytics to uncover granular—and sometimes counterintuitive—insights is a reason for optimism. If we ask the right questions, we can advance our collective understanding and address the crisis more effectively, rapidly, and comprehensively.

  1. Our analyses used only limited data sets, anonymized beforehand in full compliance with HIPAA privacy and security rules, and with permission from the state Medicaid agencies that had provided the data.
  2. There are many types of opioids. To compare dosing levels among them in an apples-to-apples way, a standard approach is to convert an opioid dose into what is called a morphine-equivalent dose (MED). (A MED is also sometimes referred to as a morphine-milligram equivalent, or MME.) For more details, see the technical appendix.
  3. See the technical appendix for a fuller discussion of episodes of care.
  4. See the technical appendix for explanation of capture rate and overall accuracy.
  5. Note that the prescription analysis was done at the level of the prescribing or rendering physician, not at the physician practice level.
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