Currently Viewing:
The American Journal of Managed Care May 2017
Drivers of Excess Costs of Opioid Abuse Among a Commercially Insured Population
Lauren M. Scarpati, PhD; Noam Y. Kirson, PhD; Miriam L. Zichlin, MPH; Zitong B. Jia, BA; Howard G. Birnbaum, PhD; and Jaren C. Howard, PharmD
Critical Incident Stress Debriefing After Adverse Patient Safety Events
Reema Harrison, PhD, MSc, BSc, and Albert Wu, MD, MPH
Assessing the Effect of the VHA PCMH Model on Utilization Patterns Among Veterans With PTSD
Ian Randall, PhD; Charles Maynard, PhD; Gary Chan, PhD; Beth Devine, PhD; and Chris Johnson, PhD
State Prescription Drug Monitoring Programs and Fatal Drug Overdoses
Young Hee Nam, PhD; Dennis G. Shea, PhD; Yunfeng Shi, PhD; and John R. Moran, PhD
Disparities in Diabetes and Hypertension Care for Individuals With Serious Mental Illness
Junqing Liu, PhD; Jonathan Brown, PhD; Suzanne Morton, MPH; D.E.B. Potter, MS; Lisa Patton, PhD; Milesh Patel, MS; Rita Lewis, MPH; and Sarah Hudson Scholle, DrPH
The Cost of Adherence Mismeasurement in Serious Mental Illness: A Claims-Based Analysis
Jason Shafrin, PhD; Felicia Forma, BSc; Ethan Scherer, PhD; Ainslie Hatch, PhD; Edward Vytlacil, PhD; and Darius Lakdawalla, PhD
Currently Reading
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 Association of Mental Health Program Characteristics and Patient Satisfaction
Austin B. Frakt, PhD; Jodie Trafton, PhD; and Steven D. Pizer, PhD
Medicaid Prior Authorization and Opioid Medication Abuse and Overdose
Gerald Cochran, PhD; Adam J. Gordon, MD, MPH; Walid F. Gellad, MD, MPH; Chung-Chou H. Chang, PhD; Wei-Hsuan Lo-Ciganic, PhD, MS, MSPharm; Carroline Lobo, MS; Evan Cole, PhD; Winfred Frazier, MD; Ping

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
A flexible population-based prescription opioid registry was established for addressing a broad range of critical public health questions relating to prescription opioid use.
ABSTRACT

Objectives: To establish a prescription opioid registry protocol in a large health system and to describe algorithms to characterize individuals using prescription opioids, opioid use episodes, and concurrent use of sedative/hypnotics. 

Study Design: Protocol development and retrospective cohort study.

Methods: Using Kaiser Permanente Northern California (KPNC) electronic health record data, we selected patients using prescription opioids in 2011. Opioid and sedative/hypnotic fills, and physical and psychiatric comorbidity diagnoses, were extracted for years 2008 to 2014. Algorithms were developed to identify each patient’s daily opioid and sedative/hypnotic use, and morphine daily-dose equivalent. Opioid episodes were classified as long-term, episodic, or acute. Logistic regression was used to predict characteristics associated with becoming a long-term opioid user. 

Results: In 2011, 18% of KPNC adult members filled at least 1 opioid prescription. Among those patients, 25% used opioids long term and their average duration of use was more than 4 years. Sedative/hypnotics were used by 76% of long-term users. Being older, white, living in a more deprived neighborhood, having a chronic pain diagnosis, and use of sedative/hypnotics were predictors of initiating long-term opioid use. 

Conclusions: This study established a population-based opioid registry that is flexible and can be used to address important questions of prescription opioid use. It will be used in future studies to answer a broad range of other critical public health issues relating to prescription opioid use.

Am J Manag Care. 2017;23(5):e146-e155
Takeaway Points

We describe a protocol for developing a prescription opioid registry using electronic health record data and algorithms to characterize individuals using prescription opioids, opioid use episodes, and concurrent use of sedative/hypnotics. 
  • In 2011, 18% of adult members of a large integrated health plan filled at least 1 opioid prescription. 
  • Among these patients, 25% used opioids long term, and the average duration of use was more than 4 years. Sedative/hypnotics were used by 76% of long-term users. 
  • Being older and white; living in a more deprived neighborhood; having chronic pain, arthritis, chronic obstructive pulmonary disease, hypertension, or a psychiatric disorder diagnosis; and a history of sedative/hypnotic use were predictors of becoming a long-term opioid user.
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. 

METHODS

Setting

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. 

Registry Inclusion

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. 

Registry Structure 

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. 

RESULTS

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up
×

Sign In

Not a member? Sign up now!