Prescription Opioid Registry Protocol in an Integrated Health System
G. Thomas Ray, MBA; Amber L. Bahorik, PhD; Paul C. VanVeldhuisen, PhD; Constance M. Weisner, DrPH, MSW; Andrea L. Rubinstein, MD; and Cynthia I. Campbell, PhD, MPH
The use of prescription opioids has increased dramatically in the past 2 decades, with associated increases in opioid misuse/abuse and opioid overdose. These are among the most commonly prescribed medications,1 with 259 million prescriptions written for opioid pain relievers in the United States in 2012.2 However, effectiveness of these medications for long-term use has not been established,3 and the risk of opioid-related abuse and overdose has led to a prescription opioid epidemic.4 Close to 2 million Americans abused or were dependent on opioids in 2014.5 From 2003 to 2013, the proportion of drug-abuse treatment admissions for nonheroin opiates tripled.6 There were more than 18,000 fatal overdoses in 2014 related to prescription opioids, more than 4 times the number in 1999.7 Sedative/hypnotics are frequently involved in overdose deaths, and their concurrent use with opioids is of high concern.8
The goal of the overall project was to use electronic health record (EHR) data from Kaiser Permanente Northern California (KPNC) to develop a patient prescription opioid registry with the potential to ultimately address several research inquiries: the characterization of opioid use and opioid users, identification of prescription opioid misuse, predictors of opioid overdose, and to describe patients’ services utilization and costs. It drew on our, and others’, previous research.9-11 Our objective here was to describe our protocol to develop the registry and to address 3 initial research questions: 1) characterize all individuals who used prescription opioids in 2011; 2) analyze their opioid use and concurrent use of sedative/hypnotics; and 3) identify predictors of becoming a new long-term user of opioids. We provide detail and context for our methodological approach, which can be a foundation for future analyses and, we hope, a methodological resource for other research teams addressing these questions.
KPNC is a nonprofit integrated healthcare delivery system providing comprehensive health services to approximately 3.8 million members in Northern California. The membership reflects the region’s general population, although it underrepresents individuals with very low levels of education and income.12 Membership includes enrollees from Medicare, Medicaid, and the State Health Insurance Assistance Program.
Data SourcesMembership, outpatient pharmacy, and medical encounter data are archived in KPNC’s EHR. Demographic (eg, age, race/ethnicity, sex, address), membership status, health services, diagnostic, and pharmacy data are regularly extracted from the EHR and stored in a Virtual Data Warehouse (VDW).13 The VDW is a distributed data model that includes EHR data and other data, such as mortality and Census data. Although the current study only uses KPNC data, the VDW is a feature of the Health Care Systems Research Network, whose data are harmonized across 19 health systems.
KPNC pharmacy data in the VDW include generic name, strength, directions for use, date dispensed, quantity dispensed, days’ supply, prescriber identification number, and National Drug Code. Surveys have found that more than 90% of members obtain all or almost all of their prescription medications through KPNC pharmacies.14 KPNC does not have policies that impose mandatory opioid dosage limits or prior authorization.
Clinical diagnostic and health services utilization data include hospitalizations, and emergency department (ED) and outpatient clinic visits (primary and specialty care). Mortality data, including date of death and underlying cause of death, are created from the EHR and death certificates. The VDW tumor registry contains information on all new cancers for KPNC members diagnosed after January 1, 1997.
We extracted all opioid fills made at KPNC outpatient pharmacies during 2011 (eAppendix Table 1 [eAppendices available at www.ajmc.com]). As prior researchers have,8,9,15,16 we focused on formulations with higher likelihood of abuse, and those used primarily to treat pain. Thus, we excluded opioid formulations used primarily as antitussives, anesthetics, antihistamines, antidiarrheals, or opioid agonists/antagonists. Eligible registry participants were aged 19 years or older (on January 1, 2011) with at least 1 opioid fill during 2011, and without a cancer diagnosis between January 1, 1997, and December 31, 2014.
To compare the demographics of individuals with and without opioid use, we selected a comparison cohort of all individuals who were: KPNC members during 2011; aged 19 years or older on January 1, 2011; without a cancer diagnosis between 1997 and 2014; and without an opioid fill in 2011.
For each individual, we extracted gender, race/ethnicity, date of birth, and home address as of January 1, 2011. Using members’ home addresses in combination with the 2006 to 2010 American Community Survey collected by the US Census Bureau,17,18 a neighborhood deprivation index (NDI) was generated at the Census tract level.19 Chi-square tests and t tests were used to identify differences (at P <.05) between the groups.
For the individuals who used prescription opioids, we extracted all outpatient opioid fills between January 1, 2008, and December 31, 2014. This allowed at least 3 years of opioid use before and after 2011, and facilitated analyses related to history and subsequent course of opioid use. We constructed a dataset of daily use: 1 record per person per day for each day of those 7 years; for each day, variables indicated if the person was considered to be using opioids, the opioid type, and the morphine equivalent of milligrams used. To identify opioid use on any given day, we assumed: 1) individuals used opioids according to the days’ supply variable (ie, according to provider instructions); 2) if a person had 7 or more days remaining on a prior fill at dispensation, the new fill was assumed to be used concurrently with the prior fill; otherwise, the new fill was assumed to be used consecutively; and 3) stock from 3 fills could be used concurrently, or be held for future use.
Opioid Episode Definition and Classification
Using the daily use dataset, we created episodes of opioid use, defined as the period from the start of any opioid use until a gap in use of more than 180 days.9-11 For each episode, we calculated: 1) episode duration; 2) number of fills; 3) prescribed days’ supply of opioids filled during the episode; 4) mean morphine daily-dose equivalent (MDDE) across the duration of the episode, by opioid type; 5) MDDE as prescribed, by opioid type (where the denominator is prescribed days’ supply). Because episodes can include gaps in use, MDDE across episodes will typically be lower than MDDE as prescribed, since the latter’s denominator does not include gaps in use.
Episodes were classified into 3 mutually exclusive types: 1) acute, 2) episodic, and 3) long-term, as in previous research.9-11 Acute episodes were those lasting less than 90 days. Episodic episodes lasted 90 days or longer, during which the individual was dispensed less than 120 days’ supply of opioids, and during which there were fewer than 10 opioid fills. Long-term episodes lasted 90 days or more with either 120 days’ supply or more of opioids, or at least 10 opioid fills. When describing episodes spanning any part of 2011, we retained only the most severe episode per individual and classified the individual according to that episode type. Thus, all individuals who had a long-term opioid episode covering any part of 2011 were classified as “persons with long-term opioid use.” Among those remaining, individuals with at least 1 episodic episode were “persons with episodic opioid use,” and the remaining individuals were “persons with acute opioid use.”
Comorbid Health Conditions, Mortality, and Sedative/Hypnotic Use
To examine comorbidities, we extracted from the VDW all diagnoses (International Classification of Diseases, 9th Revision, Clinical Modification) associated with healthcare encounters. As in prior research with other complex patient populations,20 we identified whether individuals received a diagnosis for 1 or more of 13 chronic conditions: arthritis, asthma, congestive heart failure, chronic obstructive pulmonary disease, chronic pain, diabetes, epilepsy, end-stage renal disease, hypertension, ischemic heart disease, osteoporosis, Parkinson’s disease, and stroke. We also identified individuals with the following psychiatric and substance use disorders20: attention-deficit disorders, anxiety disorder, autism, bipolar disorder, dementia, depression, other psychoses, personality disorder, schizophrenia, opioid abuse/dependence, and nonopioid substance use disorders (excluding tobacco). Date of death and underlying cause of death were also extracted, as was KPNC membership information for each month from 2008 to 2014.
For individuals with opioid use, we extracted all KPNC pharmacy fills for sedative/hypnotic medications (eAppendix Table 2) during 2008 to 2014. Using days’ supply and the same approach as for opioid fills (without calculating dose), we identified daily use of sedative/hypnotics for each person. These records were merged with the opioid episodes to calculate the number of days of sedative/hypnotic use during each opioid episode.
Predictors of Initiating Long-Term Opioid Use
Individuals who use opioids long term are at higher risk of adverse events (AEs) compared with individuals who use opioids over a shorter term.21 To identify predictors of becoming a long-term user within 3 to 4 years of starting opioid use, we identified “opioid-naïve” individuals as those without an opioid fill prior to their first fill of 2011—in other words, having had at least 3 years without prior use of prescription opioids. We required continuous KPNC membership from 2008 to 2014, or death, subsequent to their first 2011 fill (allowing gaps in membership of ≤3 months). In the year prior to their first opioid fill, we examined medical diagnoses, sedative/hypnotic use, and 4 measures of utilization as proxies for severity and propensity to use resources: 1) number of inpatient days, 2) number of ED visits, 3) number of office visits, and 4) number of nonopioid prescription fills.
We conducted logistic regression analysis with the dependent variable indicating whether or not the person subsequently had a long-term opioid episode, from initial fill to December 31, 2014. In addition to sex, age (in 7 groups), race/ethnicity, and NDI quartile, we included the following covariates measured in the year before the first opioid fill: dichotomous indicators for each of 13 chronic medical conditions; dichotomous indicators for any psychiatric disorder, opioid abuse/dependence, and nonopioid abuse/dependence; a dichotomous indicator of sedative/hypnotic use; the 4 utilization measures; and a class variable indicating the KPNC clinic where the patient received most of their care.
Among 2,480,030 adult KPNC members in 2011, 455,693 (18.3%) had at least 1 opioid fill at a KPNC pharmacy. Individuals with opioid use were different from individuals without opioid use on every characteristic, including being more likely to be female, older, white, have a chronic medical or psychiatric condition, and to have a diagnosis of opioid abuse/dependence or nonopioid substance use disorder (Table 1).
The 455,693 individuals with opioid use had 474,045 unique episodes occurring in some part of 2011. Due to the 180-day gaps used when creating episodes, few individuals had more than 1 episode in 2011: 18,352 individuals had 2 episodes covering any part of 2011, and the rest had 1. After retaining the most severe episode per person, there were 112,089 long-term opioid episodes, 71,011 episodic episodes, and 272,593 acute episodes (Table 2). Long-term users were, on average, aged 56 years, and 61% were female. Forty percent of individuals with long-term opioid use received at least 1 diagnosis for a psychiatric disorder in 2011, 3% were diagnosed with opioid abuse/dependence, and 7% were diagnosed with a nonopioid substance use disorder.
On average, long-term episodes were 1609 days long and included 54 opioid fills (Table 3). Because the opioid data spanned 2008 to 2014, it is possible that some episodes began prior to 2008 or continued after 2014; thus, episode durations may be underestimated. Among long-term opioid episodes, 27% began prior to July 1, 2008, and ended after June 30, 2014, and may be both left and right truncated. Another 17% of long-term episodes may have been left truncated only, and 24% may have been right truncated only.
Among individuals with long-term use, the mean MDDE was 38.98 mg, with the highest MDDE for long-acting Schedule II opioids. However, use of those opioids was highly skewed (median MDDE was 0), with only 28% of long-term users using any long-acting Schedule II opioids. Among individuals with episodic and acute use, on the other hand, Schedule III opioids had the highest mean MDDE. Individuals with episodic use used at lower levels than long-term users, and tended to have substantial gaps between fills; therefore, they had a much lower mean MDDE (5.37 mg). Individuals with long-term use also had higher mean MDDE than episodic or acute users.
Sedative/hypnotics were used by 76% of individuals with long-term opioid use during their episodes, and for an average of 34% of episode days. Among individuals with acute use, 16% used sedative/hypnotics during their (much shorter) acute episode.
Among all individuals using prescription opioids in 2011, 175,558 (39%) were opioid naïve. Of these, 85,305 had continuous KPNC membership from 2008 to 2014 (n = 81,809), or until death (n = 3496), and were the analytic sample for initiating long-term opioid use (Table 4). Multivariate analysis indicated that individuals at least 80 years of age were more likely to become long-term users than individuals younger than 50 years of age. Compared with whites, Asians and Hispanics were less likely to become long-term users (odds ratio [OR], 0.41; 95% confidence interval [CI], 0.35-0.47, and OR, 0.67; 95% CI, 0.60-0.75, respectively) (Table 5). Individuals in more-deprived neighborhoods were more likely to become long-term users than those in the least-deprived neighborhoods (most-deprived neighborhood: OR, 1.26; 95% CI, 1.12-1.43).
Numerous conditions were associated with long-term opioid use, including chronic pain (OR, 2.57; 95% CI, 2.26-2.93), nonopioid substance use disorders (OR, 2.25; 95% CI, 1.89-2.69), psychiatric disorders (OR, 1.22; 95% CI, 1.12-1.33), and arthritis (OR, 1.41; 95% CI, 1.31-1.52). Use of sedatives/hypnotics was associated with increased odds of becoming a long-term user (OR, 1.67; 95% CI, 1.54-1.81, vs no use). Even after adjusting for diagnosed conditions, inpatient hospital days and use of nonopioid medications in the prior year remained predictive of the long-term user. On the other hand, outpatient office visits in the prior year were associated with lower odds of becoming a long-term user.
This study developed a protocol for an EHR-based prescription opioid registry that can be used to address important research questions about prescription opioid use in noncancer patients on a population level. The current paper also addressed initial questions about the characteristics of prescription opioid users and what predicts initiating long-term use.
Consistent with prior literature,3,22,23 individuals who used opioids were older, more likely to be white, and were more clinically complex patients, with more medical and psychiatric conditions and substance use disorders than individuals not using opioids. Further, a considerable portion of patients was using opioids long term. Patients using opioids long term are especially important to identify, because duration of use is associated with abuse, overdose, and other AEs.3,16,21,24-26 As prior researchers found,22,27 we found individuals with long-term opioid use to be more likely than those with shorter-term use to have higher daily dosages, chronic medical or psychiatric conditions, and opioid or other substance use disorders. Given the current epidemic of misuse and overdose, identifying long-term users with population-based data can help health systems identify patients early, monitor them, and refer them to specialty services (eg, substance use treatment, pain management) as needed.
Our analysis of “opioid-naïve” users indicated that only 4.2% went on to long-term use within 3 years, although at any given time, the percentage of long-term opioid users is quite high (25%). Although individual risk is low, at a population level, this is consistent with the high level of AEs observed in recent years.
Predictors of developing long-term use included chronic pain, sedative/hypnotic use, psychiatric disorders, and nonopioid substance use disorders. Concurrent use of sedative/hypnotics and opioids has been shown to be associated with a substantial increased risk of death from drug overdose.8,25,26,28 Federal and health-system guidelines have focused on reducing high daily dosages, and also on restricting concurrent opioid and sedative/hypnotic use.2,29 Individuals who lived in more-deprived neighborhoods were also more likely to develop long-term opioid use; to our knowledge, this is a relationship not previously identified in other research studies. Our data do not contain information on pain severity or control. However, findings may suggest that individuals residing in more-deprived neighborhoods (which may also be a proxy for individual deprivation) have more complex health status, or fewer nonmedication treatment alternatives available—these hypotheses deserve further study.
There is increased interest in using registries to address critical clinical and policy questions.30 A goal of this project was to develop a protocol that can serve as a reference for other clinical and research teams addressing similar questions. Study algorithms can be used in health systems with pharmacy dispensation data and encounter data. For example, because our approach used the VDW, investigators from 19 other health systems in the Health Care Systems Research Network can also use the VDW to similarly address important questions about prescription opioid use. We recognize this is not without challenges, and would require adaptations, particularly for systems that have dissimilar EHR data elements or claims data. However, by sharing details about our methodology, we hope to contribute to developing harmonized approaches across systems to address the opioid epidemic.
Our measures of opioid and sedative/hypnotic use depend on pharmacy dispensation data, which is commonly used in the literature, and which we consider a reasonable proxy for use. Uncertainty also exists about calculating use for overlapping fills. However, in contrast to some prior studies, we make explicit our assumptions for overlapping fills. The vast majority of KPNC members fill prescriptions at KPNC pharmacies,14 but we miss potential non-KPNC pharmacy fills. Although all registry members filled an opioid prescription at KPNC, it is possible that individuals using opioids may be more likely to seek opioid prescriptions externally. Identification of medical and psychiatric conditions, and substance use disorders, is based on diagnoses recorded in the EHR as part of routine care; thus, individuals with more visits may have more opportunity to receive a diagnosis. Also, there can be truncation of episodes that began prior to 2008 or continued post 2014 and, therefore, possible underestimation of long-term episode duration. These limitations are similar to those of other EHR-data–based studies. Finally, generalizability to other systems may be limited, although study algorithms can be adapted.
This study established a population-based opioid registry that is flexible, and can be used to address important questions of prescription opioid use. Future analyses will leverage the prescription opioid registry and its algorithms to examine prescription opioid misuse, fatal and nonfatal overdose, and health service utilization and cost. Thus, with this same registry, we will be able to address a broad range of critical public health issues relating to prescription opioid use.
The authors gratefully acknowledge Agatha Hinman, BS, for her assistance in preparing the manuscript.