Opioid Prescribing for Chronic Pain in a Community-Based Healthcare System

This study identified populations with non-cancer chronic pain to determine which patients may be more likely to receive an opioid prescription in an outpatient setting.
Published Online: May 17, 2017
Robert J. Romanelli, PhD; Laurence I. Ikeda, MD; Braden Lynch, PharmD; Terri Craig, PharmD; Joseph C. Cappelleri, PhD; Trevor Jukes, MS; and Denis Y. Ishisaka, PharmD
ABSTRACT

Objectives: We sought to evaluate opioid prescribing in an ambulatory setting among patients with noncancer chronic pain (CP). 

Study Design: Cross-sectional analysis. 

Methods: We identified patients with at least 2 CP encounters at least 30 days apart in 2012 in the electronic health records of a community-based healthcare delivery system in northern California. We used logistic regression models to assess associations of receiving an opioid prescription with respect to number and type of CP conditions and patient demographics and characteristics. Odds ratios (ORs) with 95% confidence intervals (CIs) and the adjusted prevalence of receiving an opioid prescription were generated after controlling for important covariates. 

Results: A total of 120,481 patients with CP met eligibility criteria, with 58% receiving an opioid in 2012. The adjusted prevalence of receiving an opioid was highest for back/cervical pain (71%). The odds of receiving an opioid increased linearly with the number of CP conditions per patient (OR, 1.29; 95% CI, 1.25-1.33; P <.001). Men were generally more likely to receive an opioid than women, as were patients with noncommercial insurance, especially Medicaid (OR, 2.77; 95% CI, 2.56-3.01; P <.001) versus commercial. 

Conclusions: In an ambulatory healthcare setting, opioid prescribing to patients with CP varied by type and number of pain conditions. Opioid prescriptions to men, those with back/cervical pain, and Medicaid beneficiaries were particularly prevalent. The identification of populations more likely to receive an opioid in the treatment of CP should be of interest to healthcare systems to ensure these drugs are being used appropriately and safely. 
Takeaway Points

Opioid prescribing to patients with noncancer chronic pain (CP) in an outpatient setting varied by the number and type of pain conditions per patient, as well as by other patient characteristics. 
  • Opioid prescriptions for men, those with back/cervical pain, and Medicaid beneficiaries were particularly prevalent. 
  • Patients prescribed more than 5 medications were more likely to receive an opioid prescription than those prescribed fewer than 5 medications. 
  • The identification of populations more likely to receive an opioid in the treatment of CP is important information for healthcare systems working to ensure these drugs are being used appropriately and safely.
An estimated 25 million Americans—more than 10% of the adult population—experience pain on a daily basis.1 In the United States, opioids are one of the most commonly prescribed drugs to treat pain, with nearly 260 million prescriptions written in 2012 alone.2,3 In recent years, controversy around opioid prescribing has risen due to concerns about misuse, abuse, and diversion leading to opioid-related overdoses and deaths.4,5 In 2014, there were 18,893 opioid overdose-related deaths in the United States, for an average of 50 per day.

A systematic review conducted by the Agency for Healthcare Research and Quality in 2014, and a recent update to this review published by the CDC in 2016, showed limited evidence supporting the effectiveness of long-term opioid use for noncancer chronic pain (CP) lasting more than 3 months and indicated that risks for serious harms are dose-dependent.7,8 Based on this evidence—or lack thereof—the CDC put forth guidelines for prescribing opioids for CP, with recommendations for weighing the benefits and risks of treatment, establishing treatment goals with patients, and prescribing the lowest effective opioid dosage, among others.

Healthcare systems are challenged with ensuring that patients with CP receive appropriate treatment with positive clinical outcomes while keeping them safe from harm. Among barriers to adopting opioid practice guidelines, clinicians report inadequate training on pain management9,10 and time constraints for comprehensive opioid risk assessment and drug monitoring, especially in busy primary care practices.11,12 An understanding of populations that have a greater propensity for receiving opioid prescriptions may inform initiatives within healthcare systems to ensure that these drugs are used appropriately and safely. To our knowledge, no studies have been conducted to evaluate opioid prescribing across diverse CP conditions within a single healthcare setting. 

Previously, we established a cross-sectional cohort of adult ambulatory patients with CP in 2012 from a large community-based healthcare system in northern California to calculate the prevalence of CP overall and by various CP conditions, including back/cervical pain, arthritis/joint pain, neuropathies/neuralgias, headaches/migraines, and unclassified pain.13 In the present study, we used this cohort to examine opioid prescribing, as well as prescribing of other pain medications. We explored differences in opioid prescribing by the number and types of CP conditions per patient and by patient demographics and characteristics. 

METHODS

Study Design and Setting 

This study was conducted using Sutter Health electronic health record (EHR) data from 2012. Sutter Health is a community-based open-network healthcare system in northern California that provides ambulatory (primary and specialty) care across 130 medical clinics to approximately 3 million patients annually (10 million ambulatory visits in 2012). The system also provides care across 24 acute care hospitals, with 200,000 inpatient admissions and 800,000 emergency department visits in 2012. Similar to many other healthcare systems in the nation, Sutter Health is a mixed-payer organization with no single formulary; as such, this setting is appropriate to study drug prescribing patterns in a clinical population. Its EHR (EpicCare) is integrated across all ambulatory care clinics and hospitals. This study was approved by Sutter Health’s Institutional Review Board, and all data were de-identified in accordance with Health Insurance Portability and Accountability Act standards. 

Cohort Eligibility Criteria 

Our study cohort was inclusive of adult patients with CP with a medical record in the EHR system. First, we identified patients 18 years or older with at least 2 International Classification of Disease, 9th Revision, Clinical Modification (ICD-9 CM) diagnoses for a CP condition at least 30 days apart in 2012. The ICD-9 CM codes for chronic pain conditions, by definition, exclude acute pain conditions and were based on well-described criteria from recent health system–based studies of CP,13,14 as well as studies by White15 and Davis.16 A listing of these chronic pain diagnosis codes has been published previously.13 

We required patients to have at least 1 encounter of any type before 2010 to confirm prior contact with the health system and to further characterize comorbidities and medication utilization. We excluded patients with an encounter or problem-list diagnosis of malignancy (with the exception of nonmelanoma skin cancer) in the 2 years prior to 2012 to restrict patients from the analysis with cancer-related pain. We also excluded patients with surgery in the 3 months prior to the first CP encounter in 2012 to restrict patients from the analysis with acute postsurgical pain. 

Data Collection 

Data were collected from the EHR, including information on prescribed medications and patient demographics (age, gender, and race/ethnicity) and characteristics (insurance type and comorbidities). Patient race/ethnicity was self-reported and collected as a part of routine clinical practice according to US Census standards. We categorized race/ethnicity as Hispanic (of any race), non-Hispanic white (NHW), African American, Asian, other (Pacific Islander, American Indian/Alaskan Native, multiple races reported, or race reported as “other”), and unknown. We further disaggregated the population’s 6 largest Asian subgroups as East/Southeast Asian (Chinese, Japanese, Korean, Filipino, or Vietnamese) and Asian Indian. Charlson Comorbidity Index (CCI) scores were calculated for each patient as a measure of overall disease burden.17 Insurance type was categorized as commercial (preferred provider organization/health maintenance organization), Medicare, Medicaid, Medicare/Medicaid dual eligible, or other/unknown (including self-pay). Patients were grouped into 5 non–mutually exclusive CP categories based on anatomical location and/or pathophysiology: arthritis/joint pain, back/cervical pain, neuropathies/neuralgias, headaches/migraines, and unclassified pain (including fibromyalgia, pelvic pain, abdominal pain, and general pain). 

The primary outcome measures were medications prescribed for pain, categorized as analgesics (opioid and nonopioid agents) and nonanalgesics (benzodiazepines, antidepressants, muscle relaxants, antiepileptics, corticosteroids, antimigraine agents, antirheumatic agents, and topical agents). See the eAppendix (available at ajmc.com) for a listing of drug classes and, where appropriate, subclasses. Because data are from an open-network health system, we did not have comprehensive pharmacy claims data on this population and instead used EHR prescribing data. From a previous study of this cohort,13 56% of all outpatient visits were to a primary care physician (family or internal medicine); the remaining 44% of encounters spanned 20 different medical specialties and service lines.

Statistical Methods 

We used descriptive statistics to summarize continuous and categorical variables and logistic regression to assess associations between receiving an opioid prescription and patient demographics/characteristics.18 Models included receipt of an opioid in 2012 as the binary dependent variable. Independent variables included patient age, CCI score, total number of CP conditions by category per patient (ie, from 1-5), insurance type, and the number of other non–pain-related medications received during the study period. Because previous study results have shown differences in opioid use by gender and race/ethnicity,19-21 we also explored these differences. We combined gender and race/ethnicity as a single variable in regression models to allow for different slopes for associations between opioid prescribing and racial/ethnic group by men and women (eg, NHW men, NHW women, Hispanic men, Hispanic women). This has the same consequence as an interaction term but with simpler interpretation of model coefficients. We also included categorical dummy variables in regression models for CP categories: arthritis/joint pain, back/cervical pain, neuropathies/neuralgias, and headaches/migraines. A dummy variable for unclassified pain was excluded due to collinearity with other CP categories. 

We generated unadjusted and adjusted odds ratios (ORs) with corresponding 95% confidence intervals (CIs) for univariate and multivariate associations, respectively. We compared model coefficients within levels of the gender and racial/ethnic category by posthoc estimation of linear combinations.18 We derived the adjusted prevalence of receiving an opioid prescription from posthoc estimation of adjusted mean effects (holding constant the values of all other covariates).18 

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