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Opioid prescribing patterns among medical providers in the United States, 2003-17: retrospective, observational study

Abstract Objective To examine the distribution and patterns of opioid prescribing in the United States. Design Retrospective, observational study. Setting National private insurer covering all 50 US states and Washington DC. Participants An annual average of 669 495 providers prescribing 8.9 million opioid prescriptions to 3.9 million patients from 2003 through 2017. Main outcome measures Standardized doses of opioids in morphine milligram equivalents (MMEs) and number of opioid prescriptions. Results In 2017, the top 1% of providers accounted for 49% of all opioid doses and 27% of all opioid prescriptions. In absolute terms, the top 1% of providers prescribed an average of 748 000 MMEs—nearly 1000 times more than the middle 1%. At least half of all providers in the top 1% in one year were also in the top 1% in adjacent years. More than two fifths of all prescriptions written by the top 1% of providers were for more than 50 MMEs a day and over four fifths were for longer than seven days. In contrast, prescriptions written by the bottom 99% of providers were below these thresholds, with 86% of prescriptions for less than 50 MMEs a day and 71% for fewer than seven days. Providers prescribing high amounts of opioids and patients receiving high amounts of opioids persisted over time, with over half of both appearing in adjacent years. Conclusions Most prescriptions written by the majority of providers are under the recommended thresholds, suggesting that most US providers are careful in their prescribing. Interventions focusing on this group of providers are unlikely to effect beneficial change and could induce unnecessary burden. A large proportion of providers have established relationships with their patients over multiple years. Interventions to reduce inappropriate opioid prescribing should be focused on improving patient care, management of patients with complex pain, and reducing comorbidities rather than seeking to enforce a threshold for prescribing.

. Comparison of age and sex distribution in Optum vs the 2017 American Community Survey. 27 Table S2. Descriptive statistics of opioid prescriptions by state. 28 Table S3. Proportion of prescriptions written by all providers, the top 1% of providers, and the bottom 99% of providers that are in excess of 7 days or 50 MME per day. 29 Table S4. Ten most common recent primary diagnoses among the top centile of opioid-receiving patients, 2003-2017. 30 Table S5. Proportion of top centile of patients with any previous diagnosis of malignant cancer. 31

Online repository with reproducible code
All reproducible code can be found at our public repository: https://github.com/mkiang/disproportionate_prescribing. Whenever possible, we provide aggregated data for all analyses, including sensitivity analyses. In accordance with the Optum data use agreement, we do not provide individual-or prescription-level data and we suppress any cells based on fewer than 10 observations. Our repository provides the full reproducible pipeline such that researchers who obtain a data use agreement with Optum are able to reproduce or extend our analysis.

Gini coefficient results
We used the Gini coefficient to formally quantify the level of inequality in prescribing across the entire distribution (i.e., global inequality) rather than relative to the top centile. The Gini coefficient is a statistical measure of dispersion and takes a value of 0 when there is no inequality (i.e., all providers prescribed exactly the same amount of opioids) and 1 when there is perfect inequality (i.e., 1 provider prescribed all opioids among many potential providers).
Mathematically, the Gini coefficient (G) is defined as the relative mean difference: 44 where x is a vector of positive values, n is the number of values in x, and ̅ is the mean value of x. In our analyses, x is one of the quantiles of interest: doses per provider, number of prescriptions per provider, dose per day, dose per patient, and dose per prescription. We estimated the bootstrap confidence interval using the percentile method with 1,000 re-samples. 45 When comparing Gini coefficients, we conservatively define one as higher or lower if the confidence intervals do not overlap.
Unlike other common measures of dispersion, such as the standard deviation or coefficient of variation, the Gini coefficient is scale-invariant and bounded between 0 and 1 when the underlying distribution is positive. These properties make the Gini coefficient robust to outliers or differences in the underlying distribution and allows for valid comparison over time, across space, between specialties, or across drug types, regardless of secular trends in the volume of doses or prescriptions.
Across all study years, the Gini coefficient of opioid doses was higher benzodiazepine doses (  S16). However, the Gini coefficient did vary by provider specialty with higher inequality among primary care specialties (Fig. S17).

Sensitivity to inequality metric
To test the sensitivity of our results to the measure of inequality used, we estimated four other measures of inequality: Ricci-Schutz, Atkinson, Theil, and generalized entropy. Across all estimates of the Gini coefficient, each alternative measure was highly correlated with the Gini coefficient. Below is the correlation table, density plot, contour plot, and scatter plot across all observations. See the interactive app described below to explore other metrics.

List of individual comparison drugs
In addition to standardized benzodiazepine doses, we repeated our analyses with 18 individual drugs: • alprazolam and buspirone, two anti-anxiety medications used to treat anxiety and panic disorder; • cyclobenzaprine, a muscle relaxant used to treat pain and stiffness caused by muscle spasms; • chlordiazepoxide, used to treat anxiety disorders, alcohol withdrawal symptoms, and tremor; • clonazepam, a sedative used to treat seizures panic disorder, and anxiety; • oxazepam, used to treat anxiety (with or without depression) and alcohol withdrawal symptoms; • temazepam and triazolam, used to treat insomnia; • gabapentin, an anti-epileptic medication used to treat seizures and neuropathic pain; • lorazepam and diazepam, used to treat seizure and anxiety disorders; • dextroamphetamine and methylphenidate, two stimulants used to treat attention deficit hyperactivity disorder; • simvastatin, a common medication to reduce the risk of myocardial infarction and stroke; • citalopram, a selective serotonin reuptake inhibitor used to treat depression; • levothyroxine, used to treat hypothyroidism, enlarged thyroid glands, or thyroid cancer; • metformin, used to treat type 2 diabetes mellitus; and • warfarin, a blood thinner.
Results for each individual drug (and comparisons to standardized opioid and benzodiazepine doses) are presented through the interactive results viewer described below (Supplementary Materials §6).

Interactive results viewers
For interested readers, we have created several online applications that allow for interactively viewing results including viewing results for different provider types or states, measures, comparison drugs, and sensitivity analyses. Detailed information about these apps can be viewed at https://github.com/mkiang/disproportionate_prescribing/apps. Code for each application is available as part of the associated public repository (Supplemental Materials §1), available at: https://github.com/mkiang/disproportionate_prescribing.
Briefly, each app focuses on a specific set of analyses:

Opioid prescribing relative to 2016 CDC guidelines
In 2016, the CDC released guidelines for opioid prescribing for chronic pain. When treating new acute pain, these guidelines recommended opioid prescription durations be <3 days and only rarely >7 days, with careful justification for doses of ≥ 50 MMEs per day and rare provision of doses ≥ 90 MMEs per day. As shown in Figure S12 and Table S4  For each year, we tabulate the number of prescriptions, unique providers, and unique patients for each drug used in our analysis. Note that the y-axis is different for each panel and is on the log10 scale.

Fig. S2. Volume of opioid and benzodiazepine doses in our data set, 2003 -2017.
For each year, we tabulate the volume of opioid and benzodiazepine doses in our data set. Opioids are standardized to morphine milligram equivalents. Benzodiazepines are standardized to lorazepam milligram doses. In parentheses, we show the volume (in billions) for the last year of data, 2017. The y-axis is on the log10 scale.

Fig. S3. Proportion of standardized opioid and benzodiazepine doses prescribed by the top 1% of providers by state, 2003-2017.
Relative to benzodiazepines (blue), opioids (red) were disproportionately prescribed by a small fraction of providers.

Fig. S4. Proportion of opioid and benzodiazepine prescriptions prescribed by the top 1% of providers by state, 2003-2017.
Relative to benzodiazepines (blue), opioids (red) were disproportionately prescribed by a small fraction of providers.

Fig. S5. Most common types of opioids and benzodiazepines prescribed, 2003-2017.
For top 1% prescribers (left column) and all providers (right column), we calculate the volume of opioids (top row) and benzodiazepines (bottom row) dispensed according to individual drug. For clarity, we only plot drugs that were among the five most common types for any year between 2003 and 2017. Across all years, the most common prescribed opioid among the top 1% of providers was oxycodone hydrochloride. Among all providers, it was oxycodone hydrochloride for all years except 2007, when it was acetaminophen/hydrocodone bitartrate. For benzodiazepines, across all years and all providers, alprazolam was most common.

Fig. S6. Average amount of opioids and benzodiazepines prescribed by each of the top ten centile provider groups relative to the median (50th) centile group, 2003-2017.
For each year, we divided providers into centile groups (i.e., 100 groups of approximately equal size) based on the amount prescribed. For each centile group, we calculated the average amount prescribed and the average number of prescriptions relative to the 50th centile group. The top centile of providers prescribed far more than lower centile groups. In 2017, the top centile prescribed over 980 times more and wrote over 80 times more prescriptions than the 50th centile group. This level of prolific prescribing was not observed in benzodiazepines.

Fig. S7. Average amount of opioids and benzodiazepines prescribed by each of the top five centile provider groups, 2003-2017.
For each year, we divided providers into centiles (i.e., 100 groups of approximately equal size) based on the amount prescribed. For each centile group, we calculated the average amount prescribed and the average number of prescriptions. The top centile of providers prescribed far more than lower centile groups.

Fig. S8. Proportion of prescriptions written by each centile group in excess of 2016 CDC prescribing guidelines, 2003 -2017.
The 2016 CDC guidelines for opioid prescribing for chronic pain recommends limiting new opioid prescriptions for acute pain to <3 days and only rarely >7 days, with careful justification for doses of ≥ 50 MMEs per day and rare provision of doses ≥ 90 MMEs per day. In 2017, 98% of prescriptions written by the top centile providers were for > 7 days and 87% were for > 21 days. Similarly, 42% of all prescriptions by the top centile were for ≥ 50 MMEs per day and 23% were for ≥ 90 MMEs per day. In contrast, the vast majority of prescriptions written by the vast majority of providers in the bottom 99 centiles were below these limits.

Fig. S9. Provider specialties of the top centile of opioid prescribers, 2008-2017.
For the top centile of providers (top panel) and all providers (bottom panel), we aggregated provider specialties into 16 large groups. Family medicine specialists made up the plurality of the top 1% of specialties at 24% in 2017. Notably, pain management and rehabilitation and physical medicine specialists (PM and R) and anesthesiologists each accounted for 14% of the top centile of opioid prescribers in 2017, substantially higher than the 1% they represent across all providers. Hospice and critical care specialists accounted for less than 1% of the top centile of opioid providers. Note that specialties with less than 1%, such as critical care and hospice care, were omitted for clarity.

Fig S10. Overlap of the top centile of providers, patients, and provider-to-patient pairs across years.
For years after 2008, we calculated the proportion of the top centile of prescribers (left), patients (middle), and prescriber-to-patient dyads (right) that are in each pair of years. For each set of centiles, the highest level of overlap is noted in adjacent years with decreasing overlap in more distant years. Providers who are in the top centile of opioid prescribers in any given year are more likely than not to be in the top centile of opioid prescribers in adjacent years.

Fig. S11. Jaccard index of the top centile of providers, patients, and provider-to-patient pairs across years.
For years after 2008, we calculated the Jaccard index of the top centile of prescribers (left), patients (middle), and prescriber-to-patient pairs (right) that are in each pair of years. The Jaccard index is a formal, symmetrical measure of similarity and is defined as the size of the intersection of two sets divided by the size of the union of two sets. For each set of centiles, the highest level of Jaccard index is noted in adjacent years with decreasing values in more distant years. Providers who are in the top centile of opioid prescribers in any given year are more likely than not to be in the top centile of opioid prescribers in adjacent years. Unlike the measure of overlap used in Fig. S10, the Jaccard index is symmetrical.

Fig. S12. Proportion of schedule II opioids dispensed by the cumulative percentile of ranked providers in California, 2009.
To assess the quality of our national prescription data, we compare our numbers to a small, careful examination of California workers' compensation data set published by Swedlow et al., 31 who found the top 1%, 5%, and 10% of providers in California prescribed 42.4%, 76.3%, and 88.2%, respectively, of Schedule II opioids between January 1993 and December 2009. For each year, we estimate the Gini coefficient and bootstrapped 95% confidence interval of standardized doses and number of prescriptions for opioids (red) and benzodiazepines (blue). Opioid doses are expressed in morphine milligram equivalents and benzodiazepine doses are expressed in lorazepam milligram equivalent.

Fig. S14. Gini coefficient of opioid and benzodiazepine doses by state, 2003-2017.
For each state, we estimate the Gini coefficient of standardized doses of opioids (red) and benzodiazepines (blue). Opioid doses are expressed in morphine milligram equivalents and benzodiazepine doses are expressed in lorazepam milligram equivalent.

Fig. S15. Gini coefficient of opioid and benzodiazepine prescriptions by state, 2003-2017.
For each state, we estimate the Gini coefficient of number of prescriptions for opioids (red) and benzodiazepines (blue).

Fig. S16. State-level Gini coefficient of opioid prescriptions and age-standardized opioid mortality rate, 2003 -2017.
Each point represents one state-year with the size and color of the point corresponding to the total volume of opioids prescribed for a state in a particular year. We found no correlation between the Gini coefficient and opioid mortality (Pearson's r = 0.09). We highlight selected outliers.    Table S3. Proportion of prescriptions written by all providers, the top 1% of providers, and the bottom 99% of providers that are in excess of 7 days or 50 MME per day.
The 2016 CDC guidelines for opioid prescribing for chronic pain recommends limiting new opioid prescriptions for acute pain to shorter than 7 days and doses of less than 50 MME per day.
For each year, we show the proportion of prescriptions written by all providers (left two columns), the top 1% of providers (middle two columns) and the bottom 99% of providers (right two columns) in excess of these guidelines.

Table S4. Ten most common recent primary diagnoses among the top centile of opioidreceiving patients, 2003-2017.
For the top 1% of opioid receiving patients, we tabulated the ten most common primary diagnoses (i.e., diagnosis in the first position) for the same year and the year before.