Trevor Jukes, MS

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

 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. 

 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; 

 <.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; 

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. 

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&mdash;experience pain on a daily basis.

 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.

In recent years, controversy around opioid prescribing has risen due to concerns about misuse, abuse, and diversion leading to opioid-related overdoses and deaths.

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.

 Based on this evidence—or lack thereof&mdash;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 management

 and time constraints for comprehensive opioid risk assessment and drug monitoring, especially in busy primary care practices.

 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.

 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. 

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. 

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

) diagnoses for a CP condition at least 30 days apart in 2012. The

 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,

 A listing of these chronic pain diagnosis codes has been published previously.

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 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.

 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 

) 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,

 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.

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.

 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,

 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.

 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).

We repeated statistical models to estimate the adjusted prevalence of receiving an opioid by the number of total CP conditions for each category (eg, back/cervical pain alone, back/cervical pain plus 1 additional condition or plus 2 additional conditions). Because multiple tests were performed, we used a level of statistical significance of 0.01, instead of 0.05, to mitigate type 1 error (ie, the probability of falsely rejecting the null hypothesis of no difference). All analyses were performed in Stata version 13.0 (StataCorp; College Station, Texas). 

Among 1,784,114 adult patients with an ambulatory record in the health system, 120,481 (6.8%) met study eligibility criteria for CP, which is the estimated prevalence of these conditions during the study period.

 The most prevalent CP conditions by category were arthritis/joint pain (57%), back/cervical pain (49%), and neuropathies/neuralgias (40%) (

). Approximately 40% of patients had a total of 1 or 2 CP conditions. Patients were aged 56 years on average; the majority were women (66%) and NHW (63%). Approximately half of the cohort comprised commercially insured beneficiaries. Patients were prescribed a median of 11 unique (pain and nonpain) medications during the study period. 

Analgesic and Nonanalgesic Prescribing by CP Category 

Across all CP categories, most patients (92%) received an analgesic or nonanalgesic medication (

). Nonsteroidal anti-inflammatory drugs (NSAIDs) were the most commonly prescribed medications (68%), followed by short-acting (immediate-release) opioids (57%); 58% received a short- or long-acting (extended-release) opioid. 

Analgesics (opioid or nonopioid) (89%), any opioid (68%), and, specifically, short-acting opioids (67%) were most common in patients with back/cervical pain (Table 2). Patients received, on average, 2 analgesics during the study period; those with arthritis/joint pain and unclassified pain had the highest average number of prescriptions for analgesic medications (mean = 2.3 each for arthritis/joint pain and unclassified pain). 

Prescriptions for nonanalgesics were most common in patients with headaches/migraines (86.3%) (Table 2). Patients were prescribed, on average, 1.8 unique nonanalgesic medications; those with headaches/migraines and unclassified pain received the highest average number of nonanalgesic medications (mean = 2.6 each for arthritis/joint pain and unclassified pain). 

Opioid Prescribing by Number and Type of CP Conditions 

Among patients with a single CP type, the highest prevalence of receiving an opioid, after controlling for other covariates, was for back/cervical pain (71%) (

). For each CP category, the adjusted prevalence of receiving an opioid increased linearly as the number of CP conditions increased: arthritis/joint pain (OR, 1.39; 95% CI, 1.36-1.42; 

<.001), back/cervical pain (OR, 1.07; 95% CI, 1.05-1.09; 

 <.001), neuropathies/neuralgias (OR, 1.65; 95% CI, 1.61-1.69; 

 <.001), headaches/migraines (OR, 1.51; 95% CI, 1.47-1.56; 

 <.001), and unclassified pain (OR, 1.48; 95% CI, 1.44-1.53; 

Patient Demographics and Characteristics Associated With Receiving an Opioid Prescription 

We found several patient demographics and characteristics to be associated with receipt of an opioid prescription (

). After statistical adjustment, older patients (≥66 years vs 18-45 years), those with moderate chronic disease burden (CCI score = 2-3 vs 0), women, Asians (vs NHW), and commercially insured beneficiaries (vs all other insurance types) had lower odds of receiving an opioid. Patients with a greater total number of CP conditions had higher odds of receiving an opioid. 

Because of the potential correlation between age, CCI score, and insurance type (eg, older individuals tend to have more comorbidities and are more likely to be Medicare beneficiaries), we performed analyses to examine the effect of CCI and payer type on the relationship between age and receipt of an opioid. The presence or absence of these covariates had minimal effect on model coefficients within the age category (data not shown). 

In this cross-sectional analysis of EHR prescribing data from a community-based ambulatory healthcare system setting, we found differences in opioid prescribing to patients by number and type of CP conditions. Among patients with 1 CP condition, those with back/cervical pain had the highest prevalence of receiving an opioid, whereas those with neuropathies/neuralgias and headache/migraines had the lowest prevalence; these latter groups more frequently received nonanalgesic medications (eg, antiepileptic or antimigraine medications). The prevalence of receiving an opioid increased linearly with the number of CP conditions per patient, reaching more than 60% for those with 3, more than 70% for those with 4, and approximately 80% for those with all 5 CP conditions; however, few patients had conditions within 4 or all 5 CP categories (4.5% and 0.8%, respectively). 

Our study is consistent with previous studies showing that patients with back pain are more likely to receive an opioid than those with other pain conditions.

 Back pain is one of the most common reasons for a visit to a doctor’s office, composing approximately one-fourth of all encounters.

 Higher rates of opioid prescribing among patients with back pain may be due to the paucity of effective alternative pharmacological agents, particularly for back pain lasting more than 4 weeks.

 In general, opioid prescribing for all types of chronic musculoskeletal pain has increased 2-fold between 1980 and 2000 (8% to 16%).

These trends reflect an increased availability of newly marketed pain products in the 1990s and changes to state medical board opioid regulations around the same time, which relaxed opioid prescribing practices for CP.

 It is notable that more than half of patients with only unclassified pain—inclusive of conditions like fibromyalgia and pelvic pain&mdash;received an opioid; these patients had the highest prevalence of prescriptions for long-acting opioids (14.2%). Current guidelines do not support the use of most opioids in unclassified painful conditions where the harms may outweigh potential benefits, regardless of whether other treatments had been previously tried.

 The exception is tramadol, which has serotonin and norepinephrine reuptake inhibitory effects and is used in the treatment of fibromyalgia. 

In multivariate analyses, we found that older age and higher CCI score were negatively associated with receiving an opioid. Although older individuals may be expected to have a greater need for pain medications due to higher prevalence of painful conditions and greater overall disease burden,

 certain medications, including opioids, are not recommended in the elderly population due to increased risks.

 For example, opioid-related risks, such as respiratory depression, tend to be potentiated in older patients.

 Even minor side effects, including opioid-induced drowsiness or sedation, may have more serious consequences in this population. Similarly, older patients and those with a higher CCI may be more likely to have underlying liver or renal disease, which can impact opioid metabolism and lead to harmful effects.

We also found that for most racial/ethnic groups, men received an opioid prescription more often than women. This trend has been demonstrated in other studies of CP.

 We further showed that among men, there were no significant differences in the adjusted prevalence of receiving an opioid for NHWs, African Americans, or Hispanics; however, among women, African Americans were more likely than NHWs to receive an opioid. Notably, East/Southeast Asians and Asian Indians of both genders had the lowest prevalence of receiving opioid prescriptions. 

We are unaware of studies on this topic among Asians receiving treatment for CP in ambulatory settings, but the results of a small prospective cohort study from an inpatient postoperative setting showed that Chinese patients required lower opioid doses, yet were more likely to experience opioid-induced pruritus than a matched group of Caucasians.

 Differences in opioid metabolism have been described previously for Asians relative to Caucasians

; however, sociocultural factors may also explain disparities in opioid prescribing.

 Future studies are needed to understand whether physicians are less likely to prescribe opioids to Asians because of perceived risks or if Asians are less accepting of these medications. 

In our analysis, Medicaid beneficiaries were more likely than patients with other insurance types to receive opioids, even after controlling for other factors. CMS has reported that these beneficiaries are twice as likely to receive an opioid prescription than non-Medicaid patients.

 Another notable finding was that the number of nonpain medications prescribed was positively associated with an increase in opioid prescribing. This is contrary to expectations, but may be related to higher levels of comorbidity in patients who receive opioids. Further investigation of this finding is required.

To our knowledge, this is the first study to evaluate opioid prescribing across a range of conditions with distinct CP categories. We analyzed data from a large ambulatory care cohort and used rigorous methods to classify and identify patients with painful chronic conditions. Furthermore, data were derived from a mixed-payer organization with no single formulary, making it an appropriate setting for evaluating patterns in opioid prescribing. 

This was a cross-sectional analysis and causal inferences are restricted. Also, the study population was from a relatively small geographic area and generalizability to other parts of the United States is unknown; however, as a mixed-payer healthcare system, the setting is like many others in the nation. The findings of this study are reliant on the accuracy of, and frequency at which physicians document, information in the EHR. Because pharmacy data were not available on the majority of the population, we relied on prescribing data, meaning we cannot know what medications patients actually filled or consumed. We also cannot know whether nonanalgesic medications were prescribed to treat pain or comorbid conditions, such as epilepsy, depression, or anxiety. We did not specifically collect information on the type of provider who prescribed the drug, as the focus of this study was not on provider-level attributes of opioid prescribing. Lastly, we did not have information on over-the-counter medications, such as NSAIDs or acetaminophen, which were potentially used by patients to treat CP and may have influenced whether a prescription pain medication was warranted. 

Articles

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.

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

We sought to characterize the chronic pain (CP) population and healthcare utilization across types of CP within a community-based healthcare system. 

 Cross-sectional study of electronic health records data from 2012. 

 Patients 18 years or older with at least 2 encounter diagnoses for CP conditions in 2012 were included in the study. Patients were categorized into non—mutually exclusive CP types: arthritis/joint pain, back/cervical pain, neuropathies/neuralgias, headaches/migraines, and unclassified pain. 

 Of 1,784,114 patients, 120,481 (6.8%) met the criteria for the CP study cohort. Within the cohort, the most common types of CP were arthritis/joint pain (57%), back/cervical pain (49%), and neuropathy/neuralgias (40%). Patients with neuropathies/neuralgias were older than patients with other pain types and had more comorbidities (for neuropathies/neuralgias: mean age, 59 years; Charlson Comorbidity Index score >3, in 28% of patients). Patients with unclassified pain were most likely to be female (82%). Rates of office and emergency department (ED) visits were highest in patients with unclassified pain (5136 events and 209 events per 1000 patients, respectively). Rates of hospitalizations and 30-day hospital readmissions were highest in patients with neuropathies/neuralgias (70 events and 287 events per 1000 patients, respectively). An increased number of CP types was linearly associated with higher rates of office, ED, and hospital visits.

Based on prevalence, comorbidities, and healthcare utilization, several types of CP, including neuropathies/neuralgias, arthritis/joint pain, and unclassified pain, appear to be most impactful. Health systems can use these findings to target efforts to improve the management of patients with CP, particularly those with multiple pain-related conditions. 

Patients with chronic pain (CP) are not a homogeneous population; they differ in terms of age, sex, overall chronic disease burden, and their use of health resources. 

Certain CP conditions appear to impact the healthcare system more than others, including neuropathies/neuralgias, arthritis/joint pain, and unclassified pain. 

Patients with multiple CP conditions, regardless of type of condition, used more healthcare resources than patients with fewer CP conditions. 

Health systems can use these findings to target efforts to improve the management of patients with CP.

In the United States, approximately 100 million adults are affected by noncancer chronic pain (CP).

 CP causes substantial disability and has an impact on an individual’s productivity, functional status, and mood/mental health.

 A report from the Institute of Medicine (now the National Academy of Medicine) estimated that the annual direct and indirect costs of CP in the United States were $635 billion (in 2010 dollars)—which exceeds the costs of heart disease, cancer, and diabetes.

CP has received much attention in recent years due to its prevalence, economic impact, and burden on patients’ quality of life; additionally, an aging population has a greater propensity to develop painful conditions. Patients with CP tend to be high utilizers of healthcare resources, and painful conditions (eg, osteoarthritis, joint disorders, back pain) represent some of the most common reasons for healthcare encounters in the United States.

 Annual healthcare expenditures are, on average, nearly $5000 higher for patients with CP than for those without.

Patients with CP are not a homogenous population,

 as CP is associated with multiple disease states and patients often have more than 1 type of CP, which introduces complexity to pain management. Accordingly, targeted initiatives are needed to better identify, oversee, and coordinate care for patients with CP within the healthcare system and to direct these patients to appropriate resources to manage pain symptoms.

Few studies have evaluated how different types of CP impact the healthcare system. In this study, we sought to characterize the CP population within a community-based healthcare delivery system and to quantify healthcare utilization across types of CP within this population.

This study was conducted as a retrospective cross-sectional analysis of Sutter Health electronic health record (EHR) data from January 1 to December 31, 2012. Sutter Health is a community-based, nonprofit, open-network healthcare delivery system that provides ambulatory (primary and specialty care) and inpatient services across northern California. In 2013, the system served more than 3 million patients, with a total of 10 million outpatient visits; 800,000 emergency department (ED) visits; and nearly 200,000 hospital discharges. The EHR system, EpicCare, is integrated across ambulatory clinics and hospitals, which allows a clinician to view patient information throughout the continuum of care. The clinical population is generally representative of the underlying geographic area with respect to age, sex, and race/ethnicity.

This study was approved by the Institutional Review Board of the Palo Alto Medical Foundation Research Institute. All data were de-identified in accordance with the Health Insurance Portability and Accountability Act standards. 

Patients were included in the study cohort if they were 18 years or older, had at least 2 

) encounter diagnoses for a CP condition in 2012 that were at least 30 days apart, and had at least 1 encounter of any type prior to 2010 to confirm prior health system contact. Patients were excluded if they had an encounter or problem list diagnosis for a malignancy (other than nonmelanoma skin cancer) in the 2 years prior to 2012 or had surgery in the 3 months prior to the first CP encounter in 2012. 

Patients were grouped into 20 non—mutually exclusive CP conditions based on 

 diagnoses, which were further categorized into 5 types of CP: 1) arthritis/joint pain (arthropathy, osteoarthritis, rheumatoid arthritis, joint pain); 2) back/cervical pain (back pain, cervical radioculopathy, lumbar radiculopathy, spinal cord injury); 3) neuropathies/neuralgias (postherpetic neuropathy, diabetic neuropathy, neuropathy, neuralgia, surgically induced pain, limb pain); 4) headaches/migraines; and 5) unclassified pain (pelvic pain, abdominal pain, general pain, fibromyalgia).

 codes for CP conditions were based on well-described criteria from a recent study of CP in a single large healthcare delivery system.

 The 5 CP types were based on the anatomic location and pain pathophysiology of CP conditions.  These CP types are also based on previous research

 and were developed with input from both Sutter and Pfizer investigators, including clinical pharmacists and a physician, with the specific exclusion of cancer pain and acute pain that may be due to surgery. The 

 codes for each CP condition are provided in 

Patient demographics, clinical characteristics, and healthcare encounters (office visits, ED visits, hospitalizations, and 30-day hospital readmissions) were obtained retrospectively from the clinical EHR and from administrative databases containing medical claims. We did not restrict encounters to those only related to pain. Race/ethnicity data are from patient self-reports collected as a part of routine clinical practice and were categorized as non-Hispanic white (NHW), African American, East/Southeast Asian (Chinese, Japanese, Korean, Filipino, or Vietnamese), South Asian (Asian Indian), Hispanic (of any race), other (Pacific Islander, American Indian/Alaskan Native, of multiple races, or race reported as “other”), and unknown.

Disease burden was calculated by the Charlson Comorbidity Index (CCI) score.

 Insurance type was categorized as commercial (fee-for-service [FFS], preferred provider organization [PPO], health maintenance organization, point of service), Medicare, Medicaid, multiple payers (combination of commercial, Medicare, and/or Medicaid), other, self-pay, or unknown.

Descriptive statistics were used to summarize continuous variables (means, medians) and binary or categorical variables (relative frequency distributions) for the 5 CP types, as well as for each of the 20 CP conditions. Rates of healthcare resource utilization were calculated as the number of events per 1000 patients in 2012. We applied negative binomial regression models to assess the relationship between the primary outcome of interest, healthcare utilization (counts of office visits, ED visits, hospitalizations, or 30-day hospital readmissions in 2012), and the main predictor of interest: the number of CP types per patient taken as a nominally measured predictor (ie, total number of CP types per patient, with the reference category as 1 CP type).

Patient demographics and characteristics were included as covariates in all models for statistical adjustment (age, sex, race/ethnicity, insurance payer, prior counts of utilization, and geographic region of the clinical catchment area). In the same type of model, we further examined the linear association between healthcare utilization rates and the number of CP types by taking the latter as an ordinally—instead of nominally&mdash;measured predictor. Relative rates of each outcome were expressed as incident rate ratios (IRRs) with 95% confidence intervals (CI). A 

 value <.05 was considered statistically significant. 

We identified 1,784,114 patients who were 18 years or older and had at least 1 EHR encounter of any type in 2012; of these, 23% had at least 1 CP encounter and 11% had at least 2 CP encounters at least 30 days apart (

). Altogether, 120,481 patients (6.8%) met eligibility criteria for the CP cohort.

CP patients were, on average, aged 56 years; the majority was female (66%) and NHW (63%) (

). Most patients with CP (60%) had more than 1 type (median = 2), 57% had arthritis/joint pain, 49% had back/cervical pain, 40% had neuropathies/neuralgias, 23% had headaches/migraines, and 20% had unclassified pain. 

Patients with headaches/migraines and unclassified pain were, on average, younger (mean = 49 and 54 years, respectively) than those with other types of CP and were more likely to be female (81% and 82%, respectively) (Table). Patients with arthritis/joint pain and neuropathies/neuralgias tended to have the highest burden of comorbidity (CCI >3 in 25% and 28% of patients, respectively) compared with patients with other types of CP. Patients with headaches/migraines were the most likely to be commercially insured beneficiaries (65%) and the least likely to be Medicare beneficiaries (6%). No racial/ethnic differences were found across the types of CP. Patients with unclassified pain were most likely to have more than 1 type of CP (88%), with a median of 3 unique types. 

The relative frequency distribution of each CP condition among all CP types is shown in 

. Joint pain was the most common CP condition (41%), followed by back pain (39%). Comorbidity burden by each CP condition is provided in 

Patients with unclassified pain had the highest rates of crude (unadjusted) office visits (5136 per 1000 patients) followed by those with neuropathies/neuralgias (4865 per 1000 patients) (

). Crude ED visit rates were highest among patients with unclassified pain (209 per 1000 patients), followed by those with headaches/migraines (180 per 1000 patients).

Crude hospitalization rates were highest among patients with neuropathies/neuralgias (70 per 1000 patients), followed by those with unclassified pain (67 per 1000 patients). Among approximately 5% of patients with CP with a hospitalization in 2012, rates of 30-day readmissions were highest among patients with neuropathies/neuralgias (287 per 1000 patients), followed by those with unclassified pain (255 per 1000 patients).

The crude rates of healthcare utilization for each of the 20 CP conditions are provided in 

. Highest rates of office visits per 1000 patients were found among those with lumbar radiculopathy (6409), surgical pain (6265), neuralgia (6093), fibromyalgia (5434), and general pain (5433) (eAppendix Figure 2). Highest rates of ED visits per 1000 patients were found among those with spinal cord injury (347), general pain (226), pelvic pain (226), headaches (206), and surgical pain (205) (eAppendix Figure 3). Highest hospitalization rates per 1000 patients were found among those with spinal cord injury (179), diabetic neuropathy (130), postherpetic neuropathy (84), neuropathy (82), and osteoarthritis (80) (eAppendix Figure 4). Among patients with a hospitalization in 2012, the highest rates of 30-day readmissions per 1000 patients were found among those with neuralgia (490), diabetic neuropathy (370), rheumatoid arthritis (346), general pain (315), and neuropathy (270) (eAppendix Figure 5). 

After statistical adjustment for important covariates, an increased number of CP types per patient was associated with a higher rate of office visits (

 <.001 for all levels vs 1 CP type), ED visits (

 <.001 for all levels vs 1 CP type), and hospitalizations (

 <.001 for 2 or 3 CP types vs 1 CP type), but not 30-day hospital readmissions (

). Linear trends for the relationship between healthcare utilization rates and number of CP types per patients were statistically significant (

 <.001 each for office visits, ED visits, and hospitalizations). Unadjusted and adjusted point estimates for categorical and linear trends are provided in 

In this large, cross-sectional analysis of patients with noncancer CP from a community-based healthcare delivery system in northern California in 2012, we found that the most common type of CP was arthritis/joint pain, with more than half of all patients with CP having a diagnosis of this type (57%). Among the 20 individual CP conditions, the most common were joint pain (41%), back pain (39%), limb pain (27%), osteoarthritis (23%), and cervical radiculopathy (17%). We also found that the majority of patients with CP had more than 1 type of pain (60%), with a median of 2 unique CP types per patient; those with unclassified pain were most likely to have multiple CP types. Furthermore, patients across the various types of CP were heterogeneous with respect to age, sex, and disease burden, but were largely similar by race/ethnicity. In terms of healthcare utilization, patients with unclassified pain had the highest crude rates of office visits and ED visits, and patients with neuropathies/neuralgias had the highest crude rates of hospitalizations and 30-day hospital readmissions.

Regardless of CP type, an increased number of types of CP per patient was linearly associated with higher rates of office visits, ED visits, and hospitalizations, but not hospital readmissions. The relationship between the number of conditions and utilization rates was observed even after controlling for important drivers of resource use, including age and prior healthcare utilization. The magnitude of this relationship was most pronounced for office visits and ED visits, but was less for hospitalizations, especially for patients with all 5 CP types. We noted that less than 1% of patients had all 5 CP types (0.77%). Thus, there was limited power to detect statistically significant differences in outcomes between this group and others, particularly for relatively rare events, such as hospitalization or hospital readmissions.

Studies of CP have been previously performed in a variety of healthcare system settings

; most, however, have focused on assessing disease prevalence for individual CP conditions. Lamerato and colleagues recently showed that among patients in an integrated healthcare delivery network, the overall prevalence of CP in 2010 was approximately 10% to 12% and that the most common CP conditions were joint pain, limb pain, back pain, and osteoarthritis.

 These data are largely consistent with our findings, using a similar definition of CP; however, in their study, Lamerato and colleagues did not exclude patients with cancer-related pain or acute pain due to surgery. Nevertheless, similar to our study, the authors demonstrated that patients with CP are heterogeneous with regard to age and sex, but are largely similar across racial/ethnic groups.

 The results of ours and other studies have shown that females are disproportionately affected by CP conditions; in particular, headaches/migraines and unclassified pain, which is inclusive of pelvic pain and fibromyalgia.

Our study adds to the current body of knowledge by further demonstrating that patients across various types of CP differ with regard to overall chronic disease burden and rates of healthcare utilization. We also show that the number of concurrent CP types is linearly associated with the incident rates of office visits, ED visits, and hospitalizations. To our knowledge, our study is the first to characterize the relative use of healthcare resources across patients with different types of CP. 

Several CP types are potentially the most impactful to the healthcare system, including neuropathies/neuralgias (eg, diabetic neuropathy, neuralgia, postherpetic pain), arthritis/joint pain (eg, osteoarthritis, rheumatoid arthritis), and unclassified pain (eg, general pain, fibromyalgia). Notably, these types of CP are impactful for different reasons. Arthritis and joint pain are highly prevalent conditions, affecting a large number of patients.  However, unclassified pain affects fewer patients, who, on average, have more concurrent types of painful conditions and use more outpatient and ED services. On the other hand, neuropathies/neuralgias are relatively prevalent, but more often affect the elderly and are associated with a greater burden of comorbidity, which ostensibly drives higher hospitalization rates and readmissions.

In a 2014 study of Medicare beneficiaries, Johnston and colleagues reported higher average comorbidity index scores among patients with pain from diabetic neuropathy (mean = 3.4) compared with patients with postherpetic neuralgia or fibromyalgia (mean = 1.3, for each). The authors also found that total annual healthcare costs due to inpatient admissions, outpatient services, and outpatient prescriptions were highest among patients with diabetic neuropathy.

 Total annual healthcare costs in 2010 US dollars for patients with diabetic neuropathy were $24,740 compared with $18,320 and $16,579 for patients with fibromyalgia and postherpetic neuralgia, respectively. Approaches to mitigating the burden of CP conditions on the healthcare system will likely require individualized strategies. 

The findings of this study should be interpreted in the context of several limitations. This was a cross-sectional analysis of patients with prevalent, non—mutually exclusive types of CP. We did not have comprehensive information on disease history; thus, variation across CP types, in terms of disease comorbidity and healthcare utilization rates, may be attributable to differences in disease onset and progression.

In our analysis, approximately 7% of all patients within the healthcare system in 2012 had at least 1 of 20 CP conditions based on our eligibility criteria. Our observed point prevalence (7%) is lower than that reported by Lamerato and colleagues (10%-12%) among continuously enrolled patients from a managed-care health system in Detroit, Michigan.

 Although geographic or demographic differences between study populations may explain this discrepancy, we recognize that our cohort was different as it was derived from an open-network healthcare system with primarily FFS/PPO beneficiaries. Therefore, the prevalence of CP reported herein may be underestimated.

Identifying the appropriate “denominator” in an open-network healthcare system is problematic and requires further validation since it is difficult to determine the true annual population based on those who have utilized services. We used rather strict eligibility criteria to ensure that patients had previous contact with our health system. Although relaxing these criteria would increase the point prevalence (Figure 1), we chose higher specificity over sensitivity. Through an Internet-based nationally representative sample of adults in the United States in 2010, the prevalence of CP was estimated at 30.7%,

 which is much higher than that reported in our study and in the Lamerato study.

 This may be due to inherent methodological biases when data are collected from surveys compared with EHR or administrative claims. For example, the detection of patients with CP through encounter diagnoses is dependent on conditions being sufficiently painful to warrant medical services and the healthcare provider to appropriately code the condition.

In this study, we did not take into account reasons for healthcare encounters; thus, utilization rates across different types of CP do not necessarily reflect treatment for specific diagnoses. The use of EHR data from an open-network healthcare system may have also underestimated overall healthcare utilization rates, as patients may have received care outside of the system. However, we do not believe that use of outside resources would be different across the various types of CP. Lastly, the generalizability of the findings to other regions in the United States and to other healthcare systems (eg, closed managed-care systems or the Veterans Affairs system) is unknown.

 Palo Alto Medical Foundation Research Institute (RJR), Palo Alto, CA; Clinical Outcomes Research, Clinical Integration Department, Sutter Health (RJR, TJ, DI), Sacramento, CA; Pfizer Inc (SNS, LI, BL, TLC, JCC), New York, NY

This study was sponsored by Pfizer. Sutter Health received financial support from Pfizer in connection with this study and the development of this manuscript.

 Dr Romanelli and Mr Jukes are employees of Sutter Health, and paid consultants to Pfizer in connection with this study and the development of this manuscript. Dr Ishisaka was an employee of Sutter Health at the time the study was conducted and is currently an employee of Blue Shield of California. Drs Shah, Ikeda, Lynch, Craig, and Cappelleri are employees of Pfizer. 

 Concept and design (RJR, SNS, LI, BL, TLC, JCC, TJ, DI); acquisition of data (TJ); analysis and interpretation of data (RJR, SNS, LI, BL, TLC, JCC, TJ, DI); drafting of the manuscript (RJR, SNS, TJ, DI); critical revision of the manuscript for important intellectual content (RJR, SNS, LI, BL, TLC, JCC, DI); statistical analysis (RJR, JCC); obtaining funding (LI, BL, TLC, DI); administrative, technical, or logistic support (BL, TLC, DI); and supervision (BL, TLC, DI). 

This study describes the patient characteristics and healthcare utilization of a chronic pain population within an integrated healthcare system in northern California.

Trevor Jukes, MS

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