Abstract
Background:
Evidence exists on racial and gender disparities in chronic pain management among veterans. Most literature has described physicians’ disparate opioid prescribing patterns, although it is unknown if prescribing disparities exist among nurse practitioners (NPs) and physician assistants (PAs) or among prescription of nonopioid analgesic strategies.
Purpose:
To identify patient characteristics associated with opioid and nonopioid prescriptions among Veterans Affairs (VA) chronic pain patients by primary care physicians, NPs, and PAs.
Methodology:
We used data from the VA’s Survey of Health care Experience of Patients and Corporate Data Warehouse from October 2015 to September 2016. Outcomes included opioid and nonopioid analgesic prescriptions. Patient characteristics included race/ethnicity, gender, education level, age, and clinical characteristics (comorbidities, self-reported health, and self-reported mental health). Logistic regression was performed to test for associations of patient characteristics with outcomes.
Results:
Patients who were White, male, age 41–64 years, and with no postsecondary education had higher odds of receiving an opioid prescription (all p-values # .01), whereas patients who were Black, female, and <65 years old had higher odds of a nonopioid prescription (all p-values < .01). Having 5+ comorbidities and fair/poor self-reported health increased the odds of opioid and nonopioid prescriptions (all p-values < .01).
Conclusions:
Disparities in race, gender, and educational level significantly affect how primary care NPs, PAs, and physicians manage chronic pain.
Implications:
NPs and other primary care providers should pursue training opportunities to identify and mitigate potential biases that may affect their practice. Future research should take an intersectional lens in examining the source of chronic pain disparities.
Keywords: Analgesic, chronic pain, disparities, equity, health disparities, implicit bias, nonopioid, nurse practitioner, opioid, primary care, veterans, workforce
Introduction
Chronic musculoskeletal pain affects 100 million adult Americans throughout their lifetime (National Academy of Medicine [NAM], 2011). Compared with the general population, veterans experience 40% higher rates of chronic pain (Nahin, 2017). Veterans are also nearly twice as likely to die from accidental opioid overdose as non-veterans (Bohnert et al., 2011; Department of Veterans Affairs & Department of Defense, 2017; Gallagher, 2016). These dual realities have led to chronic pain management guidelines from the Veterans Affairs (VA) system, as well as the NAM and the Centers for Disease Control and Prevention (CDC) (CDC, 2016; Department of Veterans Affairs & Department of Defense, 2017; NAM, 2011). These guidelines recognize the role of opioid analgesics in chronic pain treatment, recommend a multimodal approach including nonopioid medications and nonpharmacologic mechanisms, and define chronic pain as a condition with physical and mental aspects that should be incorporated into treatment.
Previous studies have indicated that patient characteristics, such as race, education level, and comorbidities (including mental health status), have an impact in the management strategies used for chronic pain, especially among veterans (Burgess et al., 2014; Guy & Zhang, 2018; Schuler et al., 2021). Particularly, lower rates of opioid prescribing have been documented among Black chronic pain patients, patients with fewer physical and mental health comorbidities, and patients with a high school diploma or above compared with patients who are White, have more comorbidities, and have less than a high school diploma (Guy & Zhang, 2018; Meghani et al., 2012). These differences have been partially attributed to providers’ racist biases resulting in undertreatment of chronic pain for Black patients (Hirsh et al., 2020; Hoffman et al., 2016; Meghani et al., 2012). Qualitative evidence from Black veterans reveal perceptions of discriminatory pain management (Hausmann et al., 2020). Additionally, the lack of access to and insurance coverage of nonpharmacologic mechanisms is a potential reason for higher opioid prescribing and utilization among patients with additional comorbidities (Becker et al., 2017).
Most literature regarding the role of patient factors associated with pain management has focused on primary care physician’s prescribing patterns. In both the VA and the nongovernmental health systems, chronic pain is most often treated in primary care (Gallagher, 2016; Veterans Health Administration, 2018). Concomitantly, the number of practicing nurse practitioners (NPs) and physician assistants (PAs) in primary care have grown exponentially over the past decade (Auerbach et al., 2020), and these providers have taken on an increasingly large share of primary care management. Yet, little is known on whether pain management disparities persist among patients of NPs and PAs; such evidence is necessary for the examination and elimination of disparities in treatment. Studies examining prescribing patterns of various primary care provider (PCP) types have typically been limited to provider group comparisons without examining the differential impact of patient characteristics across provider groups, which may have a more important role to play than provider type itself (Ellenbogen & Segal, 2020; Fink et al., 2017; Lozada et al, 2020; Muench et al., 2019). Additionally, most literature has focused on opioid prescribing, with little examination if disparities exist in nonopioid mechanisms of chronic pain treatment. As a result, the purpose of this study was to (1) identify patient demographic and clinical characteristics associated with opioid and nonopioid prescriptions among VA primary care patients with chronic pain and (2) compare the differential impact of these patient characteristics on opioid and nonopioid prescriptions among VA patients whose primary care was provided by physicians, NPs, and PAs.
Methods
Design
We used a descriptive, correlational design; the details of the research design, measures, and data source for the secondary analysis have been previously reported (Nikpour et al., 2021). Briefly, we used patient and provider characteristic data from the VA’s Survey of Health care Experience of Patients and prescription and clinical data from the VA’s Corporate Data Warehouse to examine opioid and nonopioid prescriptions among VA primary care patients from October 2015 to September 2016 (FY16). For each patient, all visits to their PCP in FY16 were merged into a single summary record, allowing us to consider the patient’s opioid and nonopioid prescription history and timing during FY16. Providers were grouped as MDs, NPs, or PAs based on their reported type; when provider type was unavailable, we categorized providers according to their position title.
Sample
Eligible patients included military veterans, who had an assigned PCP at their primary VA facility who treated the patient for one or more of the following four chronic pain conditions during FY16: back pain, lower back pain, neck pain, or osteoarthritis. We excluded patients with a diagnosis of liver failure, renal failure, or cancer, because these could affect pain medication prescriptions given, and patients in the eight states where NPs and PAs were unable to prescribe opioids in FY16.
Measures
We examined eight patient characteristics: race/ethnicity, gender, age, and level of education, number of comorbidities, number of chronic pain diagnoses, self-reported health status, and self-reported mental health status. Number of comorbidities was determined using the Elixhauser index (Elixhauser et al., 1998). We examined 26 of the 31 comorbidities included in the index after removing those indicated in our exclusion criteria. For each patient, the presence of each comorbidity was denoted as a binary outcome (0 = not present, 1 = present). Primary outcomes were absence or presence of the prescription of (1) a schedule II opioid and (2) one of five classes of nonopioid pain medications (nonsteroidal anti-inflammatory drugs, acetaminophen, antidepressants, anticonvulsants, and skeletal muscular relaxants). Antidepressants were included due to their common usage in treatment of chronic pain, particularly related to neuropathies.
Statistical analysis
Descriptive statistics were used to detail patient characteristics for the full sample and by provider group. Chi-square tests were performed to test for provider group differences in each patient characteristic, whereas multivariable logistic regression was conducted to test the association of patient characteristics and their interaction with provider group with the two primary outcomes, adjusting for the other characteristics. The initial model for each outcome included provider group, patient characteristics, and their interactions. A manual backward elimination method was applied to reduce each initial model to a final model that included provider group, patient characteristics, and significant interaction effects. Adjusted odds ratios and their 95% confidence intervals were estimated to address clinical significance.
The total sample of 39,936 along with the large sample size per provider group (MD: N = 28,558; NP: N = 8,395; and PA: 2,983) yielded at least 90% power, assuming small-to-medium effects with significance set at 0.05 per test for the bivariate analyses to compare provider groups on primary outcomes as well as the covariate-adjusted models to examine the influence of each patient characteristic and its interaction with provider group on the outcomes.
Results
Table 1 displays characteristics for the full sample and each provider group (Nikpour, et al, 2021). Most patients were White (75.9%), male (92.8%), and with postsecondary education (58.2%). Nearly three-quarters had a physician as their PCP (71.5%). Compared with NPs and PAs, physicians had significantly greater proportions of patients who were White, 65+ years of age, with postsecondary education, who reported fair/poor health status, and who reported fair/poor mental health status (all p-values < .01). NPs, compared with physicians and PAs, had significantly greater proportions of patients who were female and had 5+ Elixhauser comorbidities (both p < .01). PAs, compared with physicians and NPs, had the highest proportions of patients who were White and who had a diagnosis of osteoarthritis.
Table 1.
Patient characteristics (N = 39,936)
| Characteristic | MD, N = 28,558, n (%) | NP, N = 8,395, n (%) | PA, N = 2,983, n (%) | p-Value | A Posteriori Pairwise Contrasts |
|---|---|---|---|---|---|
| Age category | .0002 | ||||
| 18–40 years | 814 (2.9) | 291 (3.5) | 118 (4.0) | ||
| 40–64 years | 11,312 (39.6) | 3,422 (40.8) | 1,184 (39.7) | ||
| 65+ years | 16,432 (57.5) | 4,682 (55.8) | 1,681 (56.4) | MD > (NP = PA) | |
| Female gender | 1867 (6.5) | 823 (9.8) | 190 (6.4) | <.0001 | NP > (MD = PA) |
| Race/ethnicity | <.0001 | ||||
| White, NH | 21,336 (74.7) | 6,533 (77.8) | 2,451 (82.2) | PA > NP > MD | |
| Black, NH | 3,639 (12.7) | 923 (11.0) | 214 (7.2) | ||
| Hispanic | 1,466 (5.1) | 318 (3.8) | 93 (3.1) | ||
| Other, NH | 2,117 (7.4) | 621 (7.4) | 225 (7.5) | ||
| Postsecondary education | 16,598 (58.8) | 4,710 (56.85) | 1,655 (56.2) | .0003 | MD > (NP = PA) |
| Chronic pain dx | |||||
| Neck pain | 6,645 (22.9) | 1971 (23.5) | 666 (22.3) | .3763 | |
| Upper back pain | 4,291 (15.0) | 1,363 (16.2) | 468 (15.7) | .0219 | (NP > MD) = PA |
| Lower back pain | 15,331 (53.7) | 4,493 (53.5) | 1,512 (50.7) | .0075 | (MD = NP) > PA |
| Osteoarthritis | 11,765 (41.2) | 3,321 (39.6) | 1,318 (44.2) | <.0001 | PA > MD > NP |
| Multiple chronic pain dx | 7,667 (26.9) | 2,243 (26.7) | 785 (26.3) | .8148 | |
| Poor/fair health status | 12,551 (44.9) | 3,631 (44.1) | 1,215 (41.4) | .0010 | MD > (NP = PA) |
| Poor/fair mental health status | 10,355 (37.0) | 2,903 (35.3) | 979 (33.4) | <.0001 | MD > (NP = PA) |
| Total comorbidities | .0424 | ||||
| 1 comorbidity | 5,733 (20.1) | 1,619 (19.3) | 637 (21.4) | ||
| 2–4 | 17,951 (62.9) | 5,259 (62.6) | 1852 (62.1) | ||
| 5 or more | 4,874 (17.1) | 1,517 (18.1) | 494 (16.6) | NP > (MD = PA) |
Note: p-values for chi-square regression. Separate analysis per chronic pain diagnosis (dx) since more than one diagnosis may apply; All patients had at least one comorbidity; Self-reported health status and mental health status. The sign > or < indicates a pairwise contrast significant at the 0.05 level, while the sign = indicates no significant difference.
Table 2 shows the rates of each comorbidity per provider group. Comorbidities that significantly differed between groups were congestive heart failure, uncomplicated hypertension, hypothyroidism, drug abuse, and psychoses (all p-values < .05). Despite statistically significant differences in demographic and clinical characteristics between provider groups, the observed provider group differences were small (1–4%). Multivariable logistic regression results indicated no statistically significant patient characteristics-by-group interactions for opioid and nonopioid prescriptions (all p-values > .05).
Table 2.
Presence of elixhauser comorbidities
| Comorbidity | MD, N = 28,558, n (%) | NP, N = 8,395, n (%) | PA, N = 2,983, n (%) | p-Value | A Posterori Pairwise Contrasts |
|---|---|---|---|---|---|
| Congestive heart failure | 1,582 (5.5) | 526 (6.3) | 153 (5.1) | .0173 | NP > (MD = PA) |
| Cardiac arrhythmias | 4,151 (14.5) | 1,273 (15.2) | 411 (13.8) | .1462 | |
| Valvular disease | 1,020 (3.6) | 311 (3.7) | 99 (3.3) | .6149 | |
| Pulmonary circulation disorders | 514 (1.8) | 141 (1.7) | 50 (1.7) | .7086 | |
| Peripheral vascular disorders | 2,592 (9.1) | 787 (9.4) | 260 (8.7) | .5200 | |
| Hypertension, uncomplicated | 20,272 (71.0) | 5,866 (69.9) | 2052 (68.8) | .0118 | MD > (NP = PA) |
| Hypertension, complicated | 167 (0.6) | 36 (0.4) | 12 (0.4) | .1312 | |
| Paralysis | 312 (1.1) | 97 (1.2) | 37 (1.2) | .7123 | |
| Other neurological disorders | 1,237 (4.3) | 389 (4.6) | 118 (4.0) | .2562 | |
| Chronic pulmonary disease | 6,692 (23.4) | 2062 (24.6) | 716 (24.0) | .0943 | |
| Diabetes, uncomplicated | 9,709 (34.0) | 2,871 (34.2) | 976 (32.7) | .3203 | |
| Diabetes, complicated | 3,155 (11.1) | 891 (10.6) | 335 (11.2) | .4780 | |
| Hypothyroidism | 2,639 (9.2) | 864 (10.3) | 305 (10.2) | .0065 | (NP > MD) = PA |
| Peptic ulcer disease | 308 (1.1) | 110 (1.3) | 44 (1.5) | .0523 | |
| HIV/AIDS | 73 (0.3) | 14 (0.2) | 2 (0.1) | .0544 | |
| RA/collagen vascular diseases | 1,172 (4.1) | 372 (4.4) | 106 (3.6) | .1067 | |
| Coagulopathy | 405 (1.4) | 101 (1.2) | 40 (1.3) | .3260 | |
| Obesity | 8,043 (28.2) | 2,416 (28.8) | 833 (28.0) | .4950 | |
| Weight loss | 616 (2.2) | 218 (2.6) | 70 (2.4) | .0558 | |
| Fluid/electrolyte disorders | 1,214 (4.3) | 394 (4.7) | 113 (3.8) | .0742 | |
| Blood loss anemia | 99 (0.4) | 22 (0.3) | 7 (0.2) | .3333 | |
| Deficiency anemia | 786 (2.8) | 213 (2.5) | 77 (2.6) | .5216 | |
| Alcohol abuse | 2,689 (9.4) | 820 (9.8) | 288 (9.7) | .5503 | |
| Drug abuse | 1821 (6.4) | 481 (5.7) | 155 (5.2) | .0074 | MD > (NP = PA) |
| Psychoses | 1,198 (4.2) | 372 (4.4) | 96 (3.2) | .0163 | (MD = NP) > PA |
| Depression | 12,883 (45.1) | 3,882 (46.2) | 1,319 (44.2) | .0898 |
Note: N = 39,936 data available per comorbidity; Fisher’s Exact Test used when n < 5; Peptic ulcer disease excludes bleeding; The sign > or < indicates a pairwise contrast significant at the 0.05 level, while = indicates no significant difference. RA = Rheumatoid arthritis.
Table 3 provides covariate-adjusted model results for opioid prescriptions, and Table 4 provides results for nonopioid prescriptions. Patients who were White, aged 41–64 years, and with no postsecondary education had higher significantly odds of receiving an opioid prescription than their counterparts in any other racial/ethnic or age group and with at least some level of postsecondary education (all p-values < .01). Furthermore, patients who self-reported fair or poor health, had multiple chronic pain diagnoses, and had 5+ Elixhauser comorbidities had significantly higher odds of receiving an opioid (all p-values < .01).
Table 3.
Opioid prescriptions: final covariate-adjusted logistic regression
| Explanatory Variables | aOR | aOR, 95% CI | p-Value | A Posteriori Pairwise Contrasts |
|---|---|---|---|---|
| Age, in years | <.0001 | 41–64 >(65+ = 18–40) | ||
| 41–64 vs. 18–40 | 1.598 | 1.413,1.806 | <.0001 | |
| 41–64 vs. 65+ | 1.456 | 1.393,1.523 | <.0001 | |
| 65+ vs. 18–40 | 1.097 | 0.970,1.241 | .1406 | |
| Race/ethnicity | <.0001 | |||
| NH White vs. NH Black | 1.164 | 1.090,1.242 | <.0001 | NHW > (NHB = H = NHO) |
| NH White vs. Hispanic | 1.201 | 1.088,1.325 | .0003 | |
| NH White vs. NH other | 1.083 | 1.000,1.172 | .0488 | |
| NH Black vs. Hispanic | 1.032 | 0.922,1.155 | .5874 | |
| NH Black vs. NH other | 0.931 | 0.845,1.025 | .1458 | |
| NH other vs. Hispanic | 1.109 | 0.982,1.252 | .0970 | |
| No post-secondary education, Any post-secondary education | 1.054 | 1.010,1.099 | .0165 | None > any |
| Female gender | 0.992 | 0.914,1.078 | .8539 | |
| Male | ||||
| Fair or poor health | 1.579 | 1.507,1.653 | <.0001 | Fair/poor > G/VG/E |
| Good, very good, or excellent | ||||
| Fair or poor mental health | 0.991 | 0.944,1.039 | .6995 | |
| Good, very good, or excellent | ||||
| Multiple chronic pain diagnoses | 1.872 | 1.785,1.964 | <.0001 | Multiple > 1 |
| One chronic pain diagnosis | ||||
| No. of comorbidities | <.0001 | 5+ > 2–4 >1 | ||
| 2–4 vs. 1 | 1.179 | 1.118,1.243 | <.0001 | |
| 2–4 vs. 5+ | 0.767 | 0.724, 0.813 | <.0001 | |
| 5+ vs. 1 | 1.563 | 1.432,1.648 | <.0001 |
Note: N = 38,626. aOR = adjusted odds ratio; CI = confidence interval; aOR effect size cutoffs: small = 1.44; medium = 2.47, large = 4.25.
Table 4.
Non-opioid prescriptions: final covariate-adjusted logistic regression model
| Explanatory Variables | aOR | aOR, 95% CI | p-Value | A Posteriori Pairwise Contrasts |
|---|---|---|---|---|
| Age, in years | <.0001 | (18–40 = 41–64) > 65+ | ||
| 41–64 vs. 18–40 | 0.822 | 0.658,1.027 | .0844 | |
| 41–64 vs. 65+ | 2.205 | 2.065, 2.356 | <.0001 | |
| 65+ vs. 18–40 | 0.373 | 0.299, 0.465 | <.0001 | |
| Race/ethnicity | <.0001 | |||
| NH White vs. NH Black | 0.732 | 0.658, 0.814 | <.0001 | NHB> (NHW = H = NHO) |
| NH White vs. Hispanic | 0.903 | 0.779,1.047 | .1755 | |
| NH White vs. NH other | 1.058 | 0.952,1.176 | .2980 | |
| NH Black vs. Hispanic | 1.234 | 1.035,1.470 | .0190 | |
| NH Black vs. NH other | 1.445 | 1.253,1.667 | <.0001 | |
| NH other vs. Hispanic | 0.854 | 0.716,1.018 | .0778 | |
| No post-secondary education | 1.012 | 0.955,1.072 | .6954 | |
| Any post-secondary education | ||||
| Female gender | 1.269 | 1.109,1.452 | .0005 | Female > male |
| Male | ||||
| Fair or poor general health | 1.359 | 1.273,1.451 | <.0001 | Fair/poor > G/VG/E |
| Good, very good, or excellent | ||||
| Fair or poor mental health | 1.417 | 1.326,1.515 | <.0001 | |
| Good, very good, or excellent | ||||
| Multiple chronic pain diagnoses | 2.106 | 1.951,2.273 | <.0001 | Multiple > 1 |
| One chronic pain diagnosis | ||||
| No. of comorbidities | <.0001 | 5+ > 2–4 >1 | ||
| 2–4 vs. 1 | 1.417 | 1.326,1.515 | <.0001 | |
| 2–4 vs. 5+ | 0.546 | 0.496, 0.599 | <.0001 | |
| 5+ vs. 1 | 2.598 | 2.339, 2.886 | <.0001 |
Note: N = 38,626. aOR = adjusted odds ratio; 95% CI = 95% confidence interval; NHB = Non-Hispanic Black, NHO = Non-Hispanic other; NHW = Non-Hispanic White; aOR effect size cutoffs: small = 1.44; medium = 2.47, large = 4.25; H = Hispanic/Latinx.
Patients who were Black, younger than 65 years, and female had significantly higher odds of nonopioid prescriptions. Further, patients who self-reported fair/poor health and fair/poor mental health had significantly higher odds relative to those reporting good/very good/excellent health or mental health (both p < .01). Additionally, individuals with multiple chronic pain diagnoses and 5+ comorbidities had higher odds of a nonopioid prescription compared with those with fewer chronic pain diagnoses and comorbidities.
Discussion
This study examined characteristics of chronic pain patients in the VA primary care system and how they differentially influenced treatment strategies of physicians, NPs, and PAs for chronic pain. Notably, although patient characteristics differed across physicians, NPs, and PAs, the proportions of patients with each of these characteristics differed by only 1–4%, suggesting that the three provider groups have somewhat similar chronic pain patient panels. Although patient characteristics influenced prescription patterns, significant interactive effect of patient characteristics and provider group on the prescribing patters was not found. These findings suggest that patient-level factors may influence whether an opioid or nonopioid is prescribed regardless of the type of provider (Fink et al., 2017; Muench et al., 2019). Concerns regarding opioid overprescription by NPs and PAs in pain management may be addressed by noting the role that these patient factors play across groups.
Our findings also add to the literature noting critical disparities in chronic pain treatment. For example, non-Hispanic White patients were more likely to receive an opioid, whereas non-Hispanic Black patients were more likely to receive a nonopioid prescription. Evidence from other studies suggests that racial biases held by providers, including false beliefs that Black patients are less susceptible to chronic pain than White patients, are associated with fewer opioid prescriptions for Black patients compared with White patients with similar diagnoses and clinical backgrounds (Hirsh et al., 2020; Hoffman et al., 2016; Meghani et al., 2012). Given that disparities persisted regardless of the type of provider, educational practices to identify and eliminate racist biases in chronic pain must be in place across the health professions.
Additionally, patients who were middle-aged and without postsecondary education were more likely to be prescribed an opioid, and patients with fair or poor health were more likely to receive both an opioid and a nonopioid prescription. Potential reasons for these prescribing patterns should be explored further but may include that job prospects for working-age, lower-educated individuals tend to be in labor-intensive positions that increase the likelihood for workplace injuries (Ho, 2017). Individuals with lower education and fair/poor heath may also live in rural, underresourced areas where education, economic, and health resources may be less accessible, and pain may go untreated for longer periods.
Reporting one’s mental health as fair or poor was not related to opioid prescription patterns but was associated with nonopioid prescriptions. It is important to note that one of the nonopioid medication classes was antidepressants. Antidepressants are often used for certain types of chronic pain such as neuropathies, particularly among older patients and patients with multiple comorbidities, including mental health comorbidities. Additionally, guidelines for chronic pain management emphasize the roles of nonopioid pain medications and mental health care in treating chronic pain (CDC, 2016; Department of Veterans Affairs & Department of Defense, 2017; NAM, 2011). Previous evidence in nongovernmental health care data found that poor mental health was associated with higher opioid prescription rates. It is unclear why this association was not found in the VA but may reflect the VA’s model of primary care–mental health integration and higher rates of co-occurring chronic pain and major depressive disorder (Department of Veterans Affairs, 2020).
Finally, although gender was not significantly related to opioid prescriptions, women had a significantly higher probability of a nonopioid prescription. It is particularly notable that both Black individuals and female individuals had higher odds of a nonopioid prescription; this finding should be further explored using a lens of intersectionality that recognizes the unique experiences of individuals with multiple marginalized identities (Carbado et al., 2013), especially when these individuals vary markedly from their provider profile.
Implications for practice
Our study’s findings may have numerous benefits for NPs and other PCPs. A critical first step can be understanding the source of pain management disparities, particularly by race and gender, and acknowledging that these disparities may arise from unconscious biases that may affect how one cares for individuals from marginalized backgrounds (Carbado et al., 2013; Meghani et al., 2012). For example, legitimate concerns of opioid prescribing as a result of the ongoing opioid overdose epidemic may be compounded by the perception of patients—disproportionately Black patients—as “drug-seeking” (Hoffman et al., 2016). Understanding the source of these perceptions, and the factors that contribute to them, is critical for addressing disparities in care.
Furthermore, increasing utilization of interdisciplinary team-based care—particularly in chronic pain management—may lead to improved care and reduced disparities in chronic pain patients. The NAM recommends a multimodal approach to chronic pain management involving both pharmacologic and nonpharmacologic mechanisms (NAM, 2011); such a complex approach may be difficult to be carried out by a sole provider and often necessitates team-based care. Taking part in interprofessional programs such as the ECHO project may provide opportunities for NPs and other providers to function in multidisciplinary teams and most effectively care for chronic pain patients (Thies et al., 2019).
Limitations
This study has numerous strengths and limitations. To our knowledge, this study is the first to address the impact of patient-level factors, across various types of PCPs, in opioid and nonopioid prescriptions for VA patients with chronic pain. By focusing on a VA population, we were not inhibited by Medicare’s incident-to billing policy, which permits NPs to bill “incident-to” a physician and therefore potentially hides the true amount and value of care provided by NPs. However, we were limited by the age of our data (FY 2015), particularly considering the VA issued new guidelines in 2017. Furthermore, we were unable to assess patterns of nonpharmacologic pain management therapies such as referrals to physical therapy. Finally, our findings are generalizable only to the VA system.
Conclusion
Demographic and clinical characteristics of VA primary care patients have an important impact on opioid and nonopioid pain management prescriptions, and one that may outweigh the impact of provider type on opioid and nonopioid prescriptions. Disparities, particularly in race, gender, and educational level, exist in chronic pain management that should be examined and addressed with an intersectional lens as part of the VA’s chronic pain strategy.
Acknowledgments:
The authors would like to acknowledge the following individuals for their contributions to this manuscript: Eugene Oddone, MD, MHS; Cynthia Brandt, MD, MPH; Lori Bastian, MD, MPH; and Melissa Skanderson, MSW. Jacqueline Nikpour is funded by the National Institute of Nursing Research T32-NR 007104 (L. Aiken, PI).
Footnotes
Competing interests: The authors report no conflicts of interest.
References
- Auerbach DI, Buerhaus PI, & Staiger DO (2020). Implications of the rapid growth of the nurse practitioner workforce in the US. Health affairs (Project Hope), 39(2), 273–279. 10.1377/hlthaff.2019.00686 [DOI] [PubMed] [Google Scholar]
- Becker WC, Dorflinger L, Edmond SN, Islam L, Heapy AA, & Fraenkel L (2017). Barriers and facilitators to use of nonpharmacological treatments in chronic pain. BMC Family Practice, 18(1), 41. 10.1186/s12875-017-0608-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bohnert AS, Ilgen MA, Galea S, McCarthy JF, & Blow FC (2011). Accidental poisoning mortality among patients in the department of Veterans Affairs health system. Medical Care, 49(4), 393–396. 10.1097/MLR.0b013e318202aa27 [DOI] [PubMed] [Google Scholar]
- Burgess DJ, Nelson DB, Gravely AA, Bair MJ, Kerns RD, Higgins DM, van Ryn M, Farmer M, & Partin MR (2014). Racial differences in prescription of opioid analgesics for chronic noncancer pain in a national sample of veterans. The Journal of Pain, 15(4), 447–455. 10.1016/j.jpain.2013.12.010 [DOI] [PubMed] [Google Scholar]
- Carbado DW, Crenshaw KW, Mays VM, & Tomlinson B (2013). Intersectionality: Mapping the movements of a theory. Du Bois Review: Social Science Research on Race, 10(2), 303–312. 10.1017/S1742058X13000349 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Centers for Disease Control and Prevention. (2016). CDC Guideline for Prescribing Opioids for Chronic Pain. https://www.cdc.gov/drugoverdose/prescribing/guideline.html [DOI] [PubMed]
- Department of Veterans Affairs. (2020). Primary Care–Mental Health Integration (PC-MHI). https://www.patientcare.va.gov/primarycare/PCMHI.asp
- Department of Veterans Affairs, & Department of Defense. (2017). VA/DoD clinical practice guideline for Opioid. Therapy for Chronic Pain. https://www.healthquality.va.gov/guidelines/Pain/cot/VADoDOTCPG022717.pdf [Google Scholar]
- Elixhauser A, Steiner C, Harris DR, & Coffey RM (1998). Comorbidity measures for use with administrative data. Medical Care, 36(1), 8–27. 10.1097/00005650-199801000-00004 [DOI] [PubMed] [Google Scholar]
- Ellenbogen MI, & Segal JB (2020). Differences in opioid prescribing among generalist physicians, nurse practitioners, and physician assistants. Pain Medicine, 21(1), 76–83. 10.1093/pm/pnz005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fink PB, Deyo RA, Hildebran C, & Hallvik SE (2017). Opioid prescribing patterns and patient outcomes by prescriber type in the Oregon prescription drug monitoring program. Pain Medicine, 19(12), 2481–2486. 10.1093/pm/pnx283 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gallagher RM (2016). Advancing the pain agenda in the veteran population. Anesthesiology Clinics, 34(2), 357–378. 10.1016/j.anclin.2016.01.003 [DOI] [PubMed] [Google Scholar]
- Guy GP Jr., & Zhang K (2018). Opioid prescribing by specialty and volume in the U.S. American Journal of Preventive Medicine, 55(5), e153–e155. 10.1016/j.amepre.2018.06.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hausmann LRM, Jones AL, McInnes SE, & Zickmund SL (2020). Identifying healthcare experiences associated with perceptions of racial/ethnic discrimination among veterans with pain: A cross-sectional mixed methods survey. PloS One, 15(9), e0237650. 10.1371/journal.pone.0237650 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hirsh AT, Anastas TM, Miller MM, Quinn PD, & Kroenke K (2020). Patient race and opioid misuse history influence provider risk perceptions for future opioid-related problems. American Psychologist, 75(6), 784–795. 10.1037/amp0000636 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ho JY (2017). The contribution of drug overdose to educational gradients in life expectancy in the United States, 1992–2011. Demography, 54(3), 1175–1202. 10.1007/s13524-017-0565-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hoffman KM, Trawalter S, Axt JR, & Oliver MN (2016). Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proceedings of the National Academy of Sciences of the United States of America, 113(16), 4296–4301. 10.1073/pnas.1516047113 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lozada MJ, Raji MA, Goodwin JS, & Kuo YF (2020). Opioid prescribing by primary care providers: A cross-sectional analysis of nurse practitioner, physician assistant, and physician prescribing patterns. Journal of General Internal Medicine, 35(9), 2584–2592. 10.1007/s11606-020-05823-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Muench U, Spetz J, Jura M, Guo C, Thomas C, & Perloff J (2019). Opioid-prescribing outcomes of medicare beneficiaries managed by nurse practitioners and physicians. Medical Care, 57(6), 482–489. 10.1097/mlr.0000000000001126 [DOI] [PubMed] [Google Scholar]
- Nahin RL (2017). Severe pain in veterans: The effect of age and sex, and comparisons with the general population. Journal of Pain, 18(3), 247–254. 10.1016/j.jpain.2016.10.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Meghani SH, Byun E, Gallagher RM (2012). Time to take stock: a meta-analysis and systematic review of analgesic treatment disparities for pain in the United States. Pain Med, 13(2), 150–174. doi: 10.1111/j.1526-4637.2011.01310.x. [DOI] [PubMed] [Google Scholar]
- National Academy of Medicine. (2011). Relieving pain in America: A blueprint for transforming prevention, care, education, and research. National Academies Press (US) National Academy of Sciences. 10.17226/13172 [DOI] [PubMed] [Google Scholar]
- Nikpour J, Broome ME, Silva S, & Allen KD (2021). Influence of primary care provider type on chronic pain management among veterans. J Nurs Reg. In press. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schuler MS, Schell TL, & Wong EC (2021). Racial/ethnic differences in prescription opioid misuse and heroin use among a national sample, 1999–2018. Drug and Alcohol Dependence, 221, 108588. 10.1016/j.drugalcdep.2021.108588 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Thies KM, Anderson D, & Beals-Reid C (2019). Project ECHO chronic pain: A qualitative analysis of recommendations by expert faculty and the process of knowledge translation. The Journal of continuing education in the health professions, 39(3), 194–200. 10.1097/CEH.0000000000000264 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Veterans’ Health Administration. (2018). OIG determination of Veterans Health Administration’s occupational staffing shortages (18-01693-196). https://www.va.gov/oig/pubs/VAOIG-18-01693-196.pdf